Skewing of scheduled search queries

ABSTRACT

Techniques for scheduling search queries in a computing environment are disclosed. A search query scheduling system associates a first set of queries with a first skew tolerance, the first set of queries scheduled to be performed during a first period, where the first skew tolerance is based on a duration of the first period. The search query scheduling system reschedules a first subset of search queries included in the first set of queries by skewing the first subset of search queries over a first portion of the first period based on the first skew tolerance.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of the U.S. patent applicationtitled, “SKEWING OF SCHEDULED SEARCH QUERIES,” filed on Jun. 17, 2020and having Ser. No. 16/904,515, which is a continuation of U.S. patentapplication titled, “SKEWING OF SCHEDULED SEARCH QUERIES,” filed on Apr.21, 2017 and having Ser. No. 15/494,419, issued as U.S. Pat. No.10,698,895. The subject matter of these related applications are herebyincorporated herein by reference.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates generally to computer analysis ofmachine-generated data and, more specifically, to skewing of scheduledsearch queries.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the above recited features of the inventioncan be understood in detail, a more particular description of theinvention may be had by reference to embodiments, some of which areillustrated in the appended drawings. It is to be noted, however, thatthe appended drawings illustrate only typical embodiments of thisinvention and are therefore not to be considered limiting of its scope,for the invention may admit to other equally effective embodiments.

FIG. 1 illustrates a networked computer environment in which anembodiment may be implemented;

FIG. 2 illustrates a block diagram of an example data intake and querysystem in which an embodiment may be implemented;

FIG. 3 is a flow diagram that illustrates how indexers process, index,and store data received from forwarders in accordance with the disclosedembodiments;

FIG. 4 is a flow diagram that illustrates how a search head and indexersperform a search query in accordance with the disclosed embodiments;

FIG. 5 illustrates a scenario where a common customer ID is found amonglog data received from three disparate sources in accordance with thedisclosed embodiments;

FIG. 6A illustrates a search screen in accordance with the disclosedembodiments;

FIG. 6B illustrates a data summary dialog that enables a user to selectvarious data sources in accordance with the disclosed embodiments;

FIGS. 7A-7D illustrate a series of user interface screens for an exampledata model-driven report generation interface in accordance with thedisclosed embodiments;

FIG. 8 illustrates an example search query received from a client andexecuted by search peers in accordance with the disclosed embodiments;

FIG. 9A illustrates a key indicators view in accordance with thedisclosed embodiments;

FIG. 9B illustrates an incident review dashboard in accordance with thedisclosed embodiments;

FIG. 9C illustrates a proactive monitoring tree in accordance with thedisclosed embodiments;

FIG. 9D illustrates a user interface screen displaying both log data andperformance data in accordance with the disclosed embodiments;

FIG. 10 illustrates a block diagram of an example cloud-based dataintake and query system in which an embodiment may be implemented;

FIG. 11 illustrates a block diagram of an example data intake and querysystem that performs searches across external data systems in accordancewith the disclosed embodiments;

FIGS. 12-14 illustrate a series of user interface screens for an exampledata model-driven report generation interface in accordance with thedisclosed embodiments;

FIGS. 15-17 illustrate example visualizations generated by a reportingapplication in accordance with the disclosed embodiments;

FIG. 18 illustrates a block diagram of an example data intake and querysystem that includes a search query scheduling system and multiplesearch heads in accordance with the disclosed embodiments;

FIG. 19 is a more detailed illustration of the search query schedulingsystem of FIG. 18 in accordance with the disclosed embodiments;

FIGS. 20A-20B illustrate example chronographic schedule strings relatedto search queries in accordance with the disclosed embodiments;

FIG. 21 illustrates example allow skew settings related to searchqueries in accordance with the disclosed embodiments;

FIGS. 22A-22B illustrate example visualizations of system load beforeand after search query skewing is enabled in accordance with thedisclosed embodiments; and

FIG. 23 is a flow diagram of method steps for skewing search queries inaccordance with the disclosed embodiments.

DETAILED DESCRIPTION

Embodiments are described herein according to the following outline:

-   -   1.0. General Overview    -   2.0. Operating Environment        -   2.1. Host Devices        -   2.2. Client Devices        -   2.3. Client Device Applications        -   2.4. Data Server System        -   2.5. Data Ingestion            -   2.5.1. Input            -   2.5.2. Parsing            -   2.5.3. Indexing        -   2.6. Query Processing        -   2.7. Field Extraction        -   2.8. Example Search Screen        -   2.9. Data Modelling        -   2.10. Acceleration Techniques            -   2.10.1. Aggregation Technique            -   2.10.2. Keyword Index            -   2.10.3. High Performance Analytics Store            -   2.10.4. Accelerating Report Generation        -   2.11. Security Features        -   2.12. Data Center Monitoring        -   2.13. Cloud-Based System Overview        -   2.14. Searching Externally Archived Data            -   2.14.1. ERP Process Features        -   2.15. IT Service Monitoring    -   3.0. Skewing Scheduled Search Queries

1.0. General Overview

Modern data centers and other computing environments can compriseanywhere from a few host computer systems to thousands of systemsconfigured to process data, service requests from remote clients, andperform numerous other computational tasks. During operation, variouscomponents within these computing environments often generatesignificant volumes of machine-generated data. For example, machine datais generated by various components in the information technology (IT)environments, such as servers, sensors, routers, mobile devices,Internet of Things (IoT) devices, etc. Machine-generated data caninclude system logs, network packet data, sensor data, applicationprogram data, error logs, stack traces, system performance data, etc. Ingeneral, machine-generated data can also include performance data,diagnostic information, and many other types of data that can beanalyzed to diagnose performance problems, monitor user interactions,and to derive other insights.

A number of tools are available to analyze machine data, that is,machine-generated data. In order to reduce the size of the potentiallyvast amount of machine data that may be generated, many of these toolstypically pre-process the data based on anticipated data-analysis needs.For example, pre-specified data items may be extracted from the machinedata and stored in a database to facilitate efficient retrieval andanalysis of those data items at search time. However, the rest of themachine data typically is not saved and discarded during pre-processing.As storage capacity becomes progressively cheaper and more plentiful,there are fewer incentives to discard these portions of machine data andmany reasons to retain more of the data.

This plentiful storage capacity is presently making it feasible to storemassive quantities of minimally processed machine data for laterretrieval and analysis. In general, storing minimally processed machinedata and performing analysis operations at search time can providegreater flexibility because it enables an analyst to search all of themachine data, instead of searching only a pre-specified set of dataitems. This may enable an analyst to investigate different aspects ofthe machine data that previously were unavailable for analysis.

However, analyzing and searching massive quantities of machine datapresents a number of challenges. For example, a data center, servers, ornetwork appliances may generate many different types and formats ofmachine data (e.g., system logs, network packet data (e.g., wire data,etc.), sensor data, application program data, error logs, stack traces,system performance data, operating system data, virtualization data,etc.) from thousands of different components, which can collectively bevery time-consuming to analyze. In another example, mobile devices maygenerate large amounts of information relating to data accesses,application performance, operating system performance, networkperformance, etc. There can be millions of mobile devices that reportthese types of information.

These challenges can be addressed by using an event-based data intakeand query system, such as the SPLUNK® ENTERPRISE system developed bySplunk Inc. of San Francisco, Calif. The SPLUNK® ENTERPRISE system isthe leading platform for providing real-time operational intelligencethat enables organizations to collect, index, and searchmachine-generated data from various websites, applications, servers,networks, and mobile devices that power their businesses. The SPLUNK®ENTERPRISE system is particularly useful for analyzing data which iscommonly found in system log files, network data, and other data inputsources. Although many of the techniques described herein are explainedwith reference to a data intake and query system similar to the SPLUNK®ENTERPRISE system, these techniques are also applicable to other typesof data systems.

In the SPLUNK® ENTERPRISE system, machine-generated data are collectedand stored as “events”. An event comprises a portion of themachine-generated data and is associated with a specific point in time.For example, events may be derived from “time series data,” where thetime series data comprises a sequence of data points (e.g., performancemeasurements from a computer system, etc.) that are associated withsuccessive points in time. In general, each event can be associated witha timestamp that is derived from the raw data in the event, determinedthrough interpolation between temporally proximate events having knowntimestamps, or determined based on other configurable rules forassociating timestamps with events, etc.

In some instances, machine data can have a predefined format, where dataitems with specific data formats are stored at predefined locations inthe data. For example, the machine data may include data stored asfields in a database table. In other instances, machine data may nothave a predefined format, that is, the data is not at fixed, predefinedlocations, but the data does have repeatable patterns and is not random.This means that some machine data can comprise various data items ofdifferent data types and that may be stored at different locationswithin the data. For example, when the data source is an operatingsystem log, an event can include one or more lines from the operatingsystem log containing raw data that includes different types ofperformance and diagnostic information associated with a specific pointin time.

Examples of components which may generate machine data from which eventscan be derived include, but are not limited to, web servers, applicationservers, databases, firewalls, routers, operating systems, and softwareapplications that execute on computer systems, mobile devices, sensors,Internet of Things (IoT) devices, etc. The data generated by such datasources can include, for example and without limitation, server logfiles, activity log files, configuration files, messages, network packetdata, performance measurements, sensor measurements, etc.

The SPLUNK® ENTERPRISE system uses flexible schema to specify how toextract information from the event data. A flexible schema may bedeveloped and redefined as needed. Note that a flexible schema may beapplied to event data “on the fly,” when it is needed (e.g., at searchtime, index time, ingestion time, etc.). When the schema is not appliedto event data until search time it may be referred to as a “late-bindingschema.”

During operation, the SPLUNK® ENTERPRISE system starts with raw inputdata (e.g., one or more system logs, streams of network packet data,sensor data, application program data, error logs, stack traces, systemperformance data, etc.). The system divides this raw data into blocks(e.g., buckets of data, each associated with a specific time frame,etc.), and parses the raw data to produce timestamped events. The systemstores the timestamped events in a data store. The system enables usersto run queries against the stored data to, for example, retrieve eventsthat meet criteria specified in a query, such as containing certainkeywords or having specific values in defined fields. As used hereinthroughout, data that is part of an event is referred to as “eventdata”. In this context, the term “field” refers to a location in theevent data containing one or more values for a specific data item. Aswill be described in more detail herein, the fields are defined byextraction rules (e.g., regular expressions) that derive one or morevalues from the portion of raw machine data in each event that has aparticular field specified by an extraction rule. The set of values soproduced are semantically-related (such as IP address), even though theraw machine data in each event may be in different formats (e.g.,semantically-related values may be in different positions in the eventsderived from different sources).

As noted above, the SPLUNK® ENTERPRISE system utilizes a late-bindingschema to event data while performing queries on events. One aspect of alate-binding schema is applying “extraction rules” to event data toextract values for specific fields during search time. Morespecifically, the extraction rules for a field can include one or moreinstructions that specify how to extract a value for the field from theevent data. An extraction rule can generally include any type ofinstruction for extracting values from data in events. In some cases, anextraction rule comprises a regular expression where a sequence ofcharacters form a search pattern, in which case the rule is referred toas a “regex rule.” The system applies the regex rule to the event datato extract values for associated fields in the event data by searchingthe event data for the sequence of characters defined in the regex rule.

In the SPLUNK® ENTERPRISE system, a field extractor may be configured toautomatically generate extraction rules for certain field values in theevents when the events are being created, indexed, or stored, orpossibly at a later time. Alternatively, a user may manually defineextraction rules for fields using a variety of techniques. In contrastto a conventional schema for a database system, a late-binding schema isnot defined at data ingestion time. Instead, the late-binding schema canbe developed on an ongoing basis until the time a query is actuallyexecuted. This means that extraction rules for the fields in a query maybe provided in the query itself, or may be located during execution ofthe query. Hence, as a user learns more about the data in the events,the user can continue to refine the late-binding schema by adding newfields, deleting fields, or modifying the field extraction rules for usethe next time the schema is used by the system. Because the SPLUNK®ENTERPRISE system maintains the underlying raw data and useslate-binding schema for searching the raw data, it enables a user tocontinue investigating and learn valuable insights about the raw data.

In some embodiments, a common field name may be used to reference two ormore fields containing equivalent data items, even though the fields maybe associated with different types of events that possibly havedifferent data formats and different extraction rules. By enabling acommon field name to be used to identify equivalent fields fromdifferent types of events generated by disparate data sources, thesystem facilitates use of a “common information model” (CIM) across thedisparate data sources (further discussed with respect to FIG. 5 ).

The size of modern data centers and other computing environments canvary from a few computer systems to thousands of computer systems. Eachcomputer system in such computing environments is generally configuredto process data, service requests from remote clients, and performnumerous other computational tasks. During operation, the computersystems within these computing environments often generate significantvolumes of machine-generated data. This machine-generated data may bestored in one or more data stores. These data stores can be accessed viasubsequent search and analysis operations for the purpose of findingcertain patterns, trends, correlations, and other useful information.One approach for performing such search and analysis operations is toschedule certain search queries to occur periodically. For example, aset of search queries could be scheduled to occur on an ongoing basis atthe beginning of each successive search period, such as the beginning ofeach minute or the beginning of each hour.

One potential drawback with the approach described above is that thecomputing systems, data stores, and other associated network componentsencounter a significant increase in demand for computing resources andthroughput at the beginning of each search period. Once the searchqueries have finished processing, the demand for computing resources andthroughput demand reduces to a relatively low level until the beginningof the next search period. Further, in cases where the number ofscheduled search queries is sufficiently high, such as 200-400 searchqueries per minute, the computing resources and throughput demand couldtemporarily exceed the maximum capacity of the computing environment. Asa result, data transferred among computing systems and data stores maybe lost, leading to incorrect or incomplete results, reduced systemperformance, and, in extreme circumstances, failure of one or morecomputing systems, data stores, and other associated network components.

As the foregoing illustrates, what is needed in the art are moreeffective ways to schedule search queries in computing environments.

Various embodiments of the present application set forth a method forscheduling search queries in a computing environment. The methodincludes associating a first set of queries with a first searchtolerance, the first set of queries scheduled to be performed during afirst period, where the first search tolerance is based on a duration ofthe first period. The method further includes rescheduling a firstsubset of search queries included in the first set of queries by skewingthe first subset of search queries over a first portion of the firstperiod based on the first search tolerance.

Other embodiments of the present invention include, without limitation,a computer-readable medium including instructions for performing one ormore aspects of the disclosed techniques, as well as a computing devicefor performing one or more aspects of the disclosed techniques.

At least one advantage of the disclosed techniques is that searchqueries scheduled to occur simultaneously are skewed over a period oftime. As a result, the demand for computing and network resources is notconcentrated at certain instances in time, but rather is distributedover a period of time. By distributing the demand for computing andnetwork resources over time, the likelihood of network data packet lossor other failure modes due to the excess demand is reduced relative toprior approaches.

2.0. Operating Environment

FIG. 1 illustrates a networked computer system 100 in which anembodiment may be implemented. Those skilled in the art would understandthat FIG. 1 represents one example of a networked computer system andother embodiments may use different arrangements.

The networked computer system 100 comprises one or more computingdevices. These one or more computing devices comprise any combination ofhardware and software configured to implement the various logicalcomponents described herein. For example, the one or more computingdevices may include one or more memories that store instructions forimplementing the various components described herein, one or morehardware processors configured to execute the instructions stored in theone or more memories, and various data repositories in the one or morememories for storing data structures utilized and manipulated by thevarious components.

In an embodiment, one or more client devices 102 are coupled to one ormore host devices 106 and a data intake and query system 108 via one ormore networks 104. Networks 104 broadly represent one or more LANs,WANs, cellular networks (e.g., LTE, HSPA, 3G, and other cellulartechnologies), and/or networks using any of wired, wireless, terrestrialmicrowave, or satellite links, and may include the public Internet.

2.1. Host Devices

In the illustrated embodiment, a system 100 includes one or more hostdevices 106. Host devices 106 may broadly include any number ofcomputers, virtual machine instances, and/or data centers that areconfigured to host or execute one or more instances of host applications114. In general, a host device 106 may be involved, directly orindirectly, in processing requests received from client devices 102.Each host device 106 may comprise, for example, one or more of a networkdevice, a web server, an application server, a database server, etc. Acollection of host devices 106 may be configured to implement anetwork-based service. For example, a provider of a network-basedservice may configure one or more host devices 106 and host applications114 (e.g., one or more web servers, application servers, databaseservers, etc.) to collectively implement the network-based application.

In general, client devices 102 communicate with one or more hostapplications 114 to exchange information. The communication between aclient device 102 and a host application 114 may, for example, be basedon the Hypertext Transfer Protocol (HTTP) or any other network protocol.Content delivered from the host application 114 to a client device 102may include, for example, HTML documents, media content, etc. Thecommunication between a client device 102 and host application 114 mayinclude sending various requests and receiving data packets. Forexample, in general, a client device 102 or application running on aclient device may initiate communication with a host application 114 bymaking a request for a specific resource (e.g., based on an HTTPrequest), and the application server may respond with the requestedcontent stored in one or more response packets.

In the illustrated embodiment, one or more of host applications 114 maygenerate various types of performance data during operation, includingevent logs, network data, sensor data, and other types ofmachine-generated data. For example, a host application 114 comprising aweb server may generate one or more web server logs in which details ofinteractions between the web server and any number of client devices 102is recorded. As another example, a host device 106 comprising a routermay generate one or more router logs that record information related tonetwork traffic managed by the router. As yet another example, a hostapplication 114 comprising a database server may generate one or morelogs that record information related to requests sent from other hostapplications 114 (e.g., web servers or application servers) for datamanaged by the database server.

2.2. Client Devices

Client devices 102 of FIG. 1 represent any computing device capable ofinteracting with one or more host devices 106 via a network 104.Examples of client devices 102 may include, without limitation, smartphones, tablet computers, handheld computers, wearable devices, laptopcomputers, desktop computers, servers, portable media players, gamingdevices, and so forth. In general, a client device 102 can provideaccess to different content, for instance, content provided by one ormore host devices 106, etc. Each client device 102 may comprise one ormore client applications 110, described in more detail in a separatesection hereinafter.

2.3. Client Device Applications

In an embodiment, each client device 102 may host or execute one or moreclient applications 110 that are capable of interacting with one or morehost devices 106 via one or more networks 104. For instance, a clientapplication 110 may be or comprise a web browser that a user may use tonavigate to one or more websites or other resources provided by one ormore host devices 106. As another example, a client application 110 maycomprise a mobile application or “app.” For example, an operator of anetwork-based service hosted by one or more host devices 106 may makeavailable one or more mobile apps that enable users of client devices102 to access various resources of the network-based service. As yetanother example, client applications 110 may include backgroundprocesses that perform various operations without direct interactionfrom a user. A client application 110 may include a “plug-in” or“extension” to another application, such as a web browser plug-in orextension.

In an embodiment, a client application 110 may include a monitoringcomponent 112. At a high level, the monitoring component 112 comprises asoftware component or other logic that facilitates generatingperformance data related to a client device's operating state, includingmonitoring network traffic sent and received from the client device andcollecting other device and/or application-specific information.Monitoring component 112 may be an integrated component of a clientapplication 110, a plug-in, an extension, or any other type of add-oncomponent. Monitoring component 112 may also be a stand-alone process.

In one embodiment, a monitoring component 112 may be created when aclient application 110 is developed, for example, by an applicationdeveloper using a software development kit (SDK). The SDK may includecustom monitoring code that can be incorporated into the codeimplementing a client application 110. When the code is converted to anexecutable application, the custom code implementing the monitoringfunctionality can become part of the application itself.

In some cases, an SDK or other code for implementing the monitoringfunctionality may be offered by a provider of a data intake and querysystem, such as a system 108. In such cases, the provider of the system108 can implement the custom code so that performance data generated bythe monitoring functionality is sent to the system 108 to facilitateanalysis of the performance data by a developer of the clientapplication or other users.

In an embodiment, the custom monitoring code may be incorporated intothe code of a client application 110 in a number of different ways, suchas the insertion of one or more lines in the client application codethat call or otherwise invoke the monitoring component 112. As such, adeveloper of a client application 110 can add one or more lines of codeinto the client application 110 to trigger the monitoring component 112at desired points during execution of the application. Code thattriggers the monitoring component may be referred to as a monitortrigger. For instance, a monitor trigger may be included at or near thebeginning of the executable code of the client application 110 such thatthe monitoring component 112 is initiated or triggered as theapplication is launched, or included at other points in the code thatcorrespond to various actions of the client application, such as sendinga network request or displaying a particular interface.

In an embodiment, the monitoring component 112 may monitor one or moreaspects of network traffic sent and/or received by a client application110. For example, the monitoring component 112 may be configured tomonitor data packets transmitted to and/or from one or more hostapplications 114. Incoming and/or outgoing data packets can be read orexamined to identify network data contained within the packets, forexample, and other aspects of data packets can be analyzed to determinea number of network performance statistics. Monitoring network trafficmay enable information to be gathered particular to the networkperformance associated with a client application 110 or set ofapplications.

In an embodiment, network performance data refers to any type of datathat indicates information about the network and/or network performance.Network performance data may include, for instance, a URL requested, aconnection type (e.g., HTTP, HTTPS, etc.), a connection start time, aconnection end time, an HTTP status code, request length, responselength, request headers, response headers, connection status (e.g.,completion, response time(s), failure, etc.), and the like. Uponobtaining network performance data indicating performance of thenetwork, the network performance data can be transmitted to a dataintake and query system 108 for analysis.

Upon developing a client application 110 that incorporates a monitoringcomponent 112, the client application 110 can be distributed to clientdevices 102. Applications generally can be distributed to client devices102 in any manner, or they can be pre-loaded. In some cases, theapplication may be distributed to a client device 102 via an applicationmarketplace or other application distribution system. For instance, anapplication marketplace or other application distribution system mightdistribute the application to a client device based on a request fromthe client device to download the application.

Examples of functionality that enables monitoring performance of aclient device are described in U.S. patent application Ser. No.14/524,748, entitled “UTILIZING PACKET HEADERS TO MONITOR NETWORKTRAFFIC IN ASSOCIATION WITH A CLIENT DEVICE”, filed on 27 Oct. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

In an embodiment, the monitoring component 112 may also monitor andcollect performance data related to one or more aspects of theoperational state of a client application 110 and/or client device 102.For example, a monitoring component 112 may be configured to collectdevice performance information by monitoring one or more client deviceoperations, or by making calls to an operating system and/or one or moreother applications executing on a client device 102 for performanceinformation. Device performance information may include, for instance, acurrent wireless signal strength of the device, a current connectiontype and network carrier, current memory performance information, ageographic location of the device, a device orientation, and any otherinformation related to the operational state of the client device.

In an embodiment, the monitoring component 112 may also monitor andcollect other device profile information including, for example, a typeof client device, a manufacturer and model of the device, versions ofvarious software applications installed on the device, and so forth.

In general, a monitoring component 112 may be configured to generateperformance data in response to a monitor trigger in the code of aclient application 110 or other triggering application event, asdescribed above, and to store the performance data in one or more datarecords. Each data record, for example, may include a collection offield-value pairs, each field-value pair storing a particular item ofperformance data in association with a field for the item. For example,a data record generated by a monitoring component 112 may include a“networkLatency” field (not shown in the Figure) in which a value isstored. This field indicates a network latency measurement associatedwith one or more network requests. The data record may include a “state”field to store a value indicating a state of a network connection, andso forth for any number of aspects of collected performance data.

2.4. Data Server System

FIG. 2 depicts a block diagram of an exemplary data intake and querysystem 108, similar to the SPLUNK® ENTERPRISE system. System 108includes one or more forwarders 204 that receive data from a variety ofinput data sources 202, and one or more indexers 206 that process andstore the data in one or more data stores 208. These forwarders andindexers can comprise separate computer systems, or may alternativelycomprise separate processes executing on one or more computer systems.

Each data source 202 broadly represents a distinct source of data thatcan be consumed by a system 108. Examples of a data source 202 include,without limitation, data files, directories of files, data sent over anetwork, event logs, registries, etc.

During operation, the forwarders 204 identify which indexers 206 receivedata collected from a data source 202 and forward the data to theappropriate indexers. Forwarders 204 can also perform operations on thedata before forwarding, including removing extraneous data, detectingtimestamps in the data, parsing data, indexing data, routing data basedon criteria relating to the data being routed, and/or performing otherdata transformations.

In an embodiment, a forwarder 204 may comprise a service accessible toclient devices 102 and host devices 106 via a network 104. For example,one type of forwarder 204 may be capable of consuming vast amounts ofreal-time data from a potentially large number of client devices 102and/or host devices 106. The forwarder 204 may, for example, comprise acomputing device which implements multiple data pipelines or “queues” tohandle forwarding of network data to indexers 206. A forwarder 204 mayalso perform many of the functions that are performed by an indexer. Forexample, a forwarder 204 may perform keyword extractions on raw data orparse raw data to create events. A forwarder 204 may generate timestamps for events. Additionally or alternatively, a forwarder 204 mayperform routing of events to indexers. Data store 208 may contain eventsderived from machine data from a variety of sources all pertaining tothe same component in an IT environment, and this data may be producedby the machine in question or by other components in the IT environment.

2.5. Data Ingestion

FIG. 3 depicts a flow chart illustrating an example data flow performedby Data Intake and Query system 108, in accordance with the disclosedembodiments. The data flow illustrated in FIG. 3 is provided forillustrative purposes only; those skilled in the art would understandthat one or more of the steps of the processes illustrated in FIG. 3 maybe removed or the ordering of the steps may be changed. Furthermore, forthe purposes of illustrating a clear example, one or more particularsystem components are described in the context of performing variousoperations during each of the data flow stages. For example, a forwarderis described as receiving and processing data during an input phase; anindexer is described as parsing and indexing data during parsing andindexing phases; and a search head is described as performing a searchquery during a search phase. However, other system arrangements anddistributions of the processing steps across system components may beused.

2.5.1. Input

At block 302, a forwarder receives data from an input source, such as adata source 202 shown in FIG. 2 . A forwarder initially may receive thedata as a raw data stream generated by the input source. For example, aforwarder may receive a data stream from a log file generated by anapplication server, from a stream of network data from a network device,or from any other source of data. In one embodiment, a forwarderreceives the raw data and may segment the data stream into “blocks”, or“buckets,” possibly of a uniform data size, to facilitate subsequentprocessing steps.

At block 304, a forwarder or other system component annotates each blockgenerated from the raw data with one or more metadata fields. Thesemetadata fields may, for example, provide information related to thedata block as a whole and may apply to each event that is subsequentlyderived from the data in the data block. For example, the metadatafields may include separate fields specifying each of a host, a source,and a source type related to the data block. A host field may contain avalue identifying a host name or IP address of a device that generatedthe data. A source field may contain a value identifying a source of thedata, such as a pathname of a file or a protocol and port related toreceived network data. A source type field may contain a valuespecifying a particular source type label for the data. Additionalmetadata fields may also be included during the input phase, such as acharacter encoding of the data, if known, and possibly other values thatprovide information relevant to later processing steps. In anembodiment, a forwarder forwards the annotated data blocks to anothersystem component (typically an indexer) for further processing.

The SPLUNK® ENTERPRISE system allows forwarding of data from one SPLUNK®ENTERPRISE instance to another, or even to a third-party system. SPLUNK®ENTERPRISE system can employ different types of forwarders in aconfiguration.

In an embodiment, a forwarder may contain the essential componentsneeded to forward data. It can gather data from a variety of inputs andforward the data to a SPLUNK® ENTERPRISE server for indexing andsearching. It also can tag metadata (e.g., source, source type, host,etc.).

Additionally or optionally, in an embodiment, a forwarder has thecapabilities of the aforementioned forwarder as well as additionalcapabilities. The forwarder can parse data before forwarding the data(e.g., associate a time stamp with a portion of data and create anevent, etc.) and can route data based on criteria such as source or typeof event. It can also index data locally while forwarding the data toanother indexer.

2.5.2. Parsing

At block 306, an indexer receives data blocks from a forwarder andparses the data to organize the data into events. In an embodiment, toorganize the data into events, an indexer may determine a source typeassociated with each data block (e.g., by extracting a source type labelfrom the metadata fields associated with the data block, etc.) and referto a source type configuration corresponding to the identified sourcetype. The source type definition may include one or more properties thatindicate to the indexer to automatically determine the boundaries ofevents within the data. In general, these properties may include regularexpression-based rules or delimiter rules where, for example, eventboundaries may be indicated by predefined characters or characterstrings. These predefined characters may include punctuation marks orother special characters including, for example, carriage returns, tabs,spaces, line breaks, etc. If a source type for the data is unknown tothe indexer, an indexer may infer a source type for the data byexamining the structure of the data. Then, it can apply an inferredsource type definition to the data to create the events.

At block 308, the indexer determines a timestamp for each event. Similarto the process for creating events, an indexer may again refer to asource type definition associated with the data to locate one or moreproperties that indicate instructions for determining a timestamp foreach event. The properties may, for example, instruct an indexer toextract a time value from a portion of data in the event, to interpolatetime values based on timestamps associated with temporally proximateevents, to create a timestamp based on a time the event data wasreceived or generated, to use the timestamp of a previous event, or useany other rules for determining timestamps.

At block 310, the indexer associates with each event one or moremetadata fields including a field containing the timestamp (in someembodiments, a timestamp may be included in the metadata fields)determined for the event. These metadata fields may include a number of“default fields” that are associated with all events, and may alsoinclude one more custom fields as defined by a user. Similar to themetadata fields associated with the data blocks at block 304, thedefault metadata fields associated with each event may include a host,source, and source type field including or in addition to a fieldstoring the timestamp.

At block 312, an indexer may optionally apply one or moretransformations to data included in the events created at block 306. Forexample, such transformations can include removing a portion of an event(e.g., a portion used to define event boundaries, extraneous charactersfrom the event, other extraneous text, etc.), masking a portion of anevent (e.g., masking a credit card number), removing redundant portionsof an event, etc. The transformations applied to event data may, forexample, be specified in one or more configuration files and referencedby one or more source type definitions.

2.5.3. Indexing

At blocks 314 and 316, an indexer can optionally generate a keywordindex to facilitate fast keyword searching for event data. To build akeyword index, at block 314, the indexer identifies a set of keywords ineach event. At block 316, the indexer includes the identified keywordsin an index, which associates each stored keyword with referencepointers to events containing that keyword (or to locations withinevents where that keyword is located, other location identifiers, etc.).When an indexer subsequently receives a keyword-based query, the indexercan access the keyword index to quickly identify events containing thekeyword.

In some embodiments, the keyword index may include entries forname-value pairs found in events, where a name-value pair can include apair of keywords connected by a symbol, such as an equals sign or colon.This way, events containing these name-value pairs can be quicklylocated. In some embodiments, fields can automatically be generated forsome or all of the name-value pairs at the time of indexing. Forexample, if the string “dest=10.0.1.2” is found in an event, a fieldnamed “dest” may be created for the event, and assigned a value of“10.0.1.2”.

At block 318, the indexer stores the events with an associated timestampin a data store 208. Timestamps enable a user to search for events basedon a time range. In one embodiment, the stored events are organized into“buckets,” where each bucket stores events associated with a specifictime range based on the timestamps associated with each event. This maynot only improve time-based searching, but also allows for events withrecent timestamps, which may have a higher likelihood of being accessed,to be stored in a faster memory to facilitate faster retrieval. Forexample, buckets containing the most recent events can be stored inflash memory rather than on a hard disk.

Each indexer 206 may be responsible for storing and searching a subsetof the events contained in a corresponding data store 208. Bydistributing events among the indexers and data stores, the indexers cananalyze events for a query in parallel. For example, using map-reducetechniques, each indexer returns partial responses for a subset ofevents to a search head that combines the results to produce an answerfor the query. By storing events in buckets for specific time ranges, anindexer may further optimize data retrieval process by searching bucketscorresponding to time ranges that are relevant to a query.

Moreover, events and buckets can also be replicated across differentindexers and data stores to facilitate high availability and disasterrecovery as described in U.S. patent application Ser. No. 14/266,812,entitled “Site-Based Search Affinity”, filed on 30 Apr. 2014, and inU.S. patent application Ser. No. 14/266,817, entitled “Multi-SiteClustering”, also filed on 30 Apr. 2014, each of which is herebyincorporated by reference in its entirety for all purposes.

2.6. Query Processing

FIG. 4 is a flow diagram that illustrates an exemplary process that asearch head and one or more indexers may perform during a search query.At block 402, a search head receives a search query from a client. Atblock 404, the search head analyzes the search query to determine whatportion(s) of the query can be delegated to indexers and what portionsof the query can be executed locally by the search head. At block 406,the search head distributes the determined portions of the query to theappropriate indexers. In an embodiment, a search head cluster may takethe place of an independent search head where each search head in thesearch head cluster coordinates with peer search heads in the searchhead cluster to schedule jobs, replicate search results, updateconfigurations, fulfill search requests, etc. In an embodiment, thesearch head (or each search head) communicates with a master node (alsoknown as a cluster master, not shown in FIG.) that provides the searchhead with a list of indexers to which the search head can distribute thedetermined portions of the query. The master node maintains a list ofactive indexers and can also designate which indexers may haveresponsibility for responding to queries over certain sets of events. Asearch head may communicate with the master node before the search headdistributes queries to indexers to discover the addresses of activeindexers.

At block 408, the indexers to which the query was distributed, searchdata stores associated with them for events that are responsive to thequery. To determine which events are responsive to the query, theindexer searches for events that match the criteria specified in thequery. These criteria can include matching keywords or specific valuesfor certain fields. The searching operations at block 408 may use thelate-binding schema to extract values for specified fields from eventsat the time the query is processed. In an embodiment, one or more rulesfor extracting field values may be specified as part of a source typedefinition. The indexers may then either send the relevant events backto the search head, or use the events to determine a partial result, andsend the partial result back to the search head.

At block 410, the search head combines the partial results and/or eventsreceived from the indexers to produce a final result for the query. Thisfinal result may comprise different types of data depending on what thequery requested. For example, the results can include a listing ofmatching events returned by the query, or some type of visualization ofthe data from the returned events. In another example, the final resultcan include one or more calculated values derived from the matchingevents.

The results generated by the system 108 can be returned to a clientusing different techniques. For example, one technique streams resultsor relevant events back to a client in real-time as they are identified.Another technique waits to report the results to the client until acomplete set of results (which may include a set of relevant events or aresult based on relevant events) is ready to return to the client. Yetanother technique streams interim results or relevant events back to theclient in real-time until a complete set of results is ready, and thenreturns the complete set of results to the client. In another technique,certain results are stored as “search jobs” and the client may retrievethe results by referring the search jobs.

The search head can also perform various operations to make the searchmore efficient. For example, before the search head begins execution ofa query, the search head can determine a time range for the query and aset of common keywords that all matching events include. The search headmay then use these parameters to query the indexers to obtain a supersetof the eventual results. Then, during a filtering stage, the search headcan perform field-extraction operations on the superset to produce areduced set of search results. This speeds up queries that are performedon a periodic basis.

2.7. Field Extraction

The search head 210 allows users to search and visualize event dataextracted from raw machine data received from homogenous data sources.It also allows users to search and visualize event data extracted fromraw machine data received from heterogeneous data sources. The searchhead 210 includes various mechanisms, which may additionally reside inan indexer 206, for processing a query. Splunk Processing Language(SPL), used in conjunction with the SPLUNK® ENTERPRISE system, can beutilized to make a query. SPL is a pipelined search language in which aset of inputs is operated on by a first command in a command line, andthen a subsequent command following the pipe symbol “|” operates on theresults produced by the first command, and so on for additionalcommands. Other query languages, such as the Structured Query Language(“SQL”), can be used to create a query.

In response to receiving the search query, search head 210 usesextraction rules to extract values for the fields associated with afield or fields in the event data being searched. The search head 210obtains extraction rules that specify how to extract a value for certainfields from an event. Extraction rules can comprise regex rules thatspecify how to extract values for the relevant fields. In addition tospecifying how to extract field values, the extraction rules may alsoinclude instructions for deriving a field value by performing a functionon a character string or value retrieved by the extraction rule. Forexample, a transformation rule may truncate a character string, orconvert the character string into a different data format. In somecases, the query itself can specify one or more extraction rules.

The search head 210 can apply the extraction rules to event data that itreceives from indexers 206. Indexers 206 may apply the extraction rulesto events in an associated data store 208. Extraction rules can beapplied to all the events in a data store, or to a subset of the eventsthat have been filtered based on some criteria (e.g., event time stampvalues, etc.). Extraction rules can be used to extract one or morevalues for a field from events by parsing the event data and examiningthe event data for one or more patterns of characters, numbers,delimiters, etc., that indicate where the field begins and, optionally,ends.

FIG. 5 illustrates an example of raw machine data received fromdisparate data sources. In this example, a user submits an order formerchandise using a vendor's shopping application program 501 running onthe user's system. In this example, the order was not delivered to thevendor's server due to a resource exception at the destination serverthat is detected by the middleware code 502. The user then sends amessage to the customer support 503 to complain about the order failingto complete. The three systems 501, 502, and 503 are disparate systemsthat do not have a common logging format. The order application 501sends log data 504 to the SPLUNK® ENTERPRISE system in one format, themiddleware code 502 sends error log data 505 in a second format, and thesupport server 503 sends log data 506 in a third format.

Using the log data received at one or more indexers 206 from the threesystems the vendor can uniquely obtain an insight into user activity,user experience, and system behavior. The search head 210 allows thevendor's administrator to search the log data from the three systemsthat one or more indexers 206 are responsible for searching, therebyobtaining correlated information, such as the order number andcorresponding customer ID number of the person placing the order. Thesystem also allows the administrator to see a visualization of relatedevents via a user interface. The administrator can query the search head210 for customer ID field value matches across the log data from thethree systems that are stored at the one or more indexers 206. Thecustomer ID field value exists in the data gathered from the threesystems, but the customer ID field value may be located in differentareas of the data given differences in the architecture of thesystems—there is a semantic relationship between the customer ID fieldvalues generated by the three systems. The search head 210 requestsevent data from the one or more indexers 206 to gather relevant eventdata from the three systems. It then applies extraction rules to theevent data in order to extract field values that it can correlate. Thesearch head may apply a different extraction rule to each set of eventsfrom each system when the event data format differs among systems. Inthis example, the user interface can display to the administrator theevent data corresponding to the common customer ID field values 507,508, and 509, thereby providing the administrator with insight into acustomer's experience.

Note that query results can be returned to a client, a search head, orany other system component for further processing. In general, queryresults may include a set of one or more events, a set of one or morevalues obtained from the events, a subset of the values, statisticscalculated based on the values, a report containing the values, or avisualization, such as a graph or chart, generated from the values.

2.8. Example Search Screen

FIG. 6A illustrates an example search screen 600 in accordance with thedisclosed embodiments. Search screen 600 includes a search bar 602 thataccepts user input in the form of a search string. It also includes atime range picker 612 that enables the user to specify a time range forthe search. For “historical searches” the user can select a specifictime range, or alternatively a relative time range, such as “today,”“yesterday” or “last week.” For “real-time searches,” the user canselect the size of a preceding time window to search for real-timeevents. Search screen 600 also initially displays a “data summary”dialog as is illustrated in FIG. 6B that enables the user to selectdifferent sources for the event data, such as by selecting specifichosts and log files.

After the search is executed, the search screen 600 in FIG. 6A candisplay the results through search results tabs 604, wherein searchresults tabs 604 includes: an “events tab” that displays variousinformation about events returned by the search; a “statistics tab” thatdisplays statistics about the search results; and a “visualization tab”that displays various visualizations of the search results. The eventstab illustrated in FIG. 6A displays a timeline graph 605 thatgraphically illustrates the number of events that occurred in one-hourintervals over the selected time range. It also displays an events list608 that enables a user to view the raw data in each of the returnedevents. It additionally displays a fields sidebar 606 that includesstatistics about occurrences of specific fields in the returned events,including “selected fields” that are pre-selected by the user, and“interesting fields” that are automatically selected by the system basedon pre-specified criteria.

2.9. Data Models

A data model is a hierarchically structured search-time mapping ofsemantic knowledge about one or more datasets. It encodes the domainknowledge necessary to build a variety of specialized searches of thosedatasets. Those searches, in turn, can be used to generate reports.

A data model is composed of one or more “objects” (or “data modelobjects”) that define or otherwise correspond to a specific set of data.

Objects in data models can be arranged hierarchically in parent/childrelationships. Each child object represents a subset of the datasetcovered by its parent object. The top-level objects in data models arecollectively referred to as “root objects.”

Child objects have inheritance. Data model objects are defined bycharacteristics that mostly break down into constraints and attributes.Child objects inherit constraints and attributes from their parentobjects and have additional constraints and attributes of their own.Child objects provide a way of filtering events from parent objects.Because a child object always provides an additional constraint inaddition to the constraints it has inherited from its parent object, thedataset it represents is always a subset of the dataset that its parentrepresents.

For example, a first data model object may define a broad set of datapertaining to e-mail activity generally, and another data model objectmay define specific datasets within the broad dataset, such as a subsetof the e-mail data pertaining specifically to e-mails sent. Examples ofdata models can include electronic mail, authentication, databases,intrusion detection, malware, application state, alerts, computeinventory, network sessions, network traffic, performance, audits,updates, vulnerabilities, etc. Data models and their objects can bedesigned by knowledge managers in an organization, and they can enabledownstream users to quickly focus on a specific set of data. Forexample, a user can simply select an “e-mail activity” data model objectto access a dataset relating to e-mails generally (e.g., sent orreceived), or select an “e-mails sent” data model object (or datasub-model object) to access a dataset relating to e-mails sent.

A data model object may be defined by (1) a set of search constraints,and (2) a set of fields. Thus, a data model object can be used toquickly search data to identify a set of events and to identify a set offields to be associated with the set of events. For example, an “e-mailssent” data model object may specify a search for events relating toe-mails that have been sent, and specify a set of fields that areassociated with the events. Thus, a user can retrieve and use the“e-mails sent” data model object to quickly search source data forevents relating to sent e-mails, and may be provided with a listing ofthe set of fields relevant to the events in a user interface screen.

A child of the parent data model may be defined by a search (typically anarrower search) that produces a subset of the events that would beproduced by the parent data model's search. The child's set of fieldscan include a subset of the set of fields of the parent data modeland/or additional fields. Data model objects that reference the subsetscan be arranged in a hierarchical manner, so that child subsets ofevents are proper subsets of their parents. A user iteratively applies amodel development tool (not shown in FIG.) to prepare a query thatdefines a subset of events and assigns an object name to that subset. Achild subset is created by further limiting a query that generated aparent subset. A late-binding schema of field extraction rules isassociated with each object or subset in the data model.

Data definitions in associated schemas can be taken from the commoninformation model (CIM) or can be devised for a particular schema andoptionally added to the CIM. Child objects inherit fields from parentsand can include fields not present in parents. A model developer canselect fewer extraction rules than are available for the sourcesreturned by the query that defines events belonging to a model.Selecting a limited set of extraction rules can be a tool forsimplifying and focusing the data model, while allowing a userflexibility to explore the data subset. Development of a data model isfurther explained in U.S. Pat. Nos. 8,788,525 and 8,788,526, bothentitled “DATA MODEL FOR MACHINE DATA FOR SEMANTIC SEARCH”, both issuedon 22 Jul. 2014, U.S. Pat. No. 8,983,994, entitled “GENERATION OF A DATAMODEL FOR SEARCHING MACHINE DATA”, issued on 17 Mar. 2015, U.S. patentapplication Ser. No. 14/611,232, entitled “GENERATION OF A DATA MODELAPPLIED TO QUERIES”, filed on 31 Jan. 2015, and U.S. patent applicationSer. No. 14/815,884, entitled “GENERATION OF A DATA MODEL APPLIED TOOBJECT QUERIES”, filed on 31 Jul. 2015, each of which is herebyincorporated by reference in its entirety for all purposes. See, also,Knowledge Manager Manual, Build a Data Model, Splunk Enterprise 6.1.3pp. 150-204 (Aug. 25, 2014).

A data model can also include reports. One or more report formats can beassociated with a particular data model and be made available to runagainst the data model. A user can use child objects to design reportswith object datasets that already have extraneous data pre-filtered out.In an embodiment, the data intake and query system 108 provides the userwith the ability to produce reports (e.g., a table, chart,visualization, etc.) without having to enter SPL, SQL, or other querylanguage terms into a search screen. Data models are used as the basisfor the search feature.

Data models may be selected in a report generation interface. The reportgenerator supports drag-and-drop organization of fields to be summarizedin a report. When a model is selected, the fields with availableextraction rules are made available for use in the report. The user mayrefine and/or filter search results to produce more precise reports. Theuser may select some fields for organizing the report and select otherfields for providing detail according to the report organization. Forexample, “region” and “salesperson” are fields used for organizing thereport and sales data can be summarized (subtotaled and totaled) withinthis organization. The report generator allows the user to specify oneor more fields within events and apply statistical analysis on valuesextracted from the specified one or more fields. The report generatormay aggregate search results across sets of events and generatestatistics based on aggregated search results. Building reports usingthe report generation interface is further explained in U.S. patentapplication Ser. No. 14/503,335, entitled “GENERATING REPORTS FROMUNSTRUCTURED DATA”, filed on 30 Sep. 2014, and which is herebyincorporated by reference in its entirety for all purposes, and in PivotManual, Splunk Enterprise 6.1.3 (Aug. 4, 2014). Data visualizations alsocan be generated in a variety of formats, by reference to the datamodel. Reports, data visualizations, and data model objects can be savedand associated with the data model for future use. The data model objectmay be used to perform searches of other data.

FIGS. 12, 13, and 7A-7D illustrate a series of user interface screenswhere a user may select report generation options using data models. Thereport generation process may be driven by a predefined data modelobject, such as a data model object defined and/or saved via a reportingapplication or a data model object obtained from another source. A usercan load a saved data model object using a report editor. For example,the initial search query and fields used to drive the report editor maybe obtained from a data model object. The data model object that is usedto drive a report generation process may define a search and a set offields. Upon loading of the data model object, the report generationprocess may enable a user to use the fields (e.g., the fields defined bythe data model object) to define criteria for a report (e.g., filters,split rows/columns, aggregates, etc.) and the search may be used toidentify events (e.g., to identify events responsive to the search) usedto generate the report. That is, for example, if a data model object isselected to drive a report editor, the graphical user interface of thereport editor may enable a user to define reporting criteria for thereport using the fields associated with the selected data model object,and the events used to generate the report may be constrained to theevents that match, or otherwise satisfy, the search constraints of theselected data model object.

The selection of a data model object for use in driving a reportgeneration may be facilitated by a data model object selectioninterface. FIG. 12 illustrates an example interactive data modelselection graphical user interface 1200 of a report editor that displaysa listing of available data models 1201. The user may select one of thedata models 1202.

FIG. 13 illustrates an example data model object selection graphicaluser interface 1300 that displays available data objects 1301 for theselected data object model 1202. The user may select one of thedisplayed data model objects 1302 for use in driving the reportgeneration process.

Once a data model object is selected by the user, a user interfacescreen 700 shown in FIG. 7A may display an interactive listing ofautomatic field identification options 701 based on the selected datamodel object. For example, a user may select one of the threeillustrated options (e.g., the “All Fields” option 702, the “SelectedFields” option 703, or the “Coverage” option (e.g., fields with at leasta specified % of coverage) 704). If the user selects the “All Fields”option 702, all of the fields identified from the events that werereturned in response to an initial search query may be selected. Thatis, for example, all of the fields of the identified data model objectfields may be selected. If the user selects the “Selected Fields” option703, only the fields from the fields of the identified data model objectfields that are selected by the user may be used. If the user selectsthe “Coverage” option 704, only the fields of the identified data modelobject fields meeting a specified coverage criteria may be selected. Apercent coverage may refer to the percentage of events returned by theinitial search query that a given field appears in. Thus, for example,if an object dataset includes 10,000 events returned in response to aninitial search query, and the “avg_age” field appears in 854 of those10,000 events, then the “avg_age” field would have a coverage of 8.54%for that object dataset. If, for example, the user selects the“Coverage” option and specifies a coverage value of 2%, only fieldshaving a coverage value equal to or greater than 2% may be selected. Thenumber of fields corresponding to each selectable option may bedisplayed in association with each option. For example, “97” displayednext to the “All Fields” option 702 indicates that 97 fields will beselected if the “All Fields” option is selected. The “3” displayed nextto the “Selected Fields” option 703 indicates that 3 of the 97 fieldswill be selected if the “Selected Fields” option is selected. The “49”displayed next to the “Coverage” option 704 indicates that 49 of the 97fields (e.g., the 49 fields having a coverage of 2% or greater) will beselected if the “Coverage” option is selected. The number of fieldscorresponding to the “Coverage” option may be dynamically updated basedon the specified percent of coverage.

FIG. 7B illustrates an example graphical user interface screen (alsocalled the pivot interface) 705 displaying the reporting application's“Report Editor” page. The screen may display interactive elements fordefining various elements of a report. For example, the page includes a“Filters” element 706, a “Split Rows” element 707, a “Split Columns”element 708, and a “Column Values” element 709. The page may include alist of search results 711. In this example, the Split Rows element 707is expanded, revealing a listing of fields 710 that can be used todefine additional criteria (e.g., reporting criteria). The listing offields 710 may correspond to the selected fields (attributes). That is,the listing of fields 710 may list only the fields previously selected,either automatically and/or manually by a user. FIG. 7C illustrates aformatting dialogue 712 that may be displayed upon selecting a fieldfrom the listing of fields 710. The dialogue can be used to format thedisplay of the results of the selection (e.g., label the column to bedisplayed as “component”).

FIG. 7D illustrates an example graphical user interface screen 705including a table of results 713 based on the selected criteriaincluding splitting the rows by the “component” field. A column 714having an associated count for each component listed in the table may bedisplayed that indicates an aggregate count of the number of times thatthe particular field-value pair (e.g., the value in a row) occurs in theset of events responsive to the initial search query.

FIG. 14 illustrates an example graphical user interface screen 1400 thatallows the user to filter search results and to perform statisticalanalysis on values extracted from specific fields in the set of events.In this example, the top ten product names ranked by price are selectedas a filter 1401 that causes the display of the ten most popularproducts sorted by price. Each row is displayed by product name andprice 1402. This results in each product displayed in a column labeled“product name” along with an associated price in a column labeled“price” 1406. Statistical analysis of other fields in the eventsassociated with the ten most popular products have been specified ascolumn values 1403. A count of the number of successful purchases foreach product is displayed in column 1404. This statistics may beproduced by filtering the search results by the product name, findingall occurrences of a successful purchase in a field within the eventsand generating a total of the number of occurrences. A sum of the totalsales is displayed in column 1405, which is a result of themultiplication of the price and the number of successful purchases foreach product.

The reporting application allows the user to create graphicalvisualizations of the statistics generated for a report. For example,FIG. 15 illustrates an example graphical user interface 1500 thatdisplays a set of components and associated statistics 1501. Thereporting application allows the user to select a visualization of thestatistics in a graph (e.g., bar chart, scatter plot, area chart, linechart, pie chart, radial gauge, marker gauge, filler gauge, etc.). FIG.16 illustrates an example of a bar chart visualization 1600 of an aspectof the statistical data 1501. FIG. 17 illustrates a scatter plotvisualization 1700 of an aspect of the statistical data 1501.

2.10. Acceleration Technique

The above-described system provides significant flexibility by enablinga user to analyze massive quantities of minimally processed data “on thefly” at search time instead of storing pre-specified portions of thedata in a database at ingestion time. This flexibility enables a user tosee valuable insights, correlate data, and perform subsequent queries toexamine interesting aspects of the data that may not have been apparentat ingestion time.

However, performing extraction and analysis operations at search timecan involve a large amount of data and require a large number ofcomputational operations, which can cause delays in processing thequeries. Advantageously, SPLUNK® ENTERPRISE system employs a number ofunique acceleration techniques that have been developed to speed upanalysis operations performed at search time. These techniques include:(1) performing search operations in parallel across multiple indexers;(2) using a keyword index; (3) using a high performance analytics store;and (4) accelerating the process of generating reports. These noveltechniques are described in more detail below.

2.10.1. Aggregation Technique

To facilitate faster query processing, a query can be structured suchthat multiple indexers perform the query in parallel, while aggregationof search results from the multiple indexers is performed locally at thesearch head. For example, FIG. 8 illustrates how a search query 802received from a client at a search head 210 can split into two phases,including: (1) subtasks 804 (e.g., data retrieval or simple filtering)that may be performed in parallel by indexers 206 for execution, and (2)a search results aggregation operation 806 to be executed by the searchhead when the results are ultimately collected from the indexers.

During operation, upon receiving search query 802, a search head 210determines that a portion of the operations involved with the searchquery may be performed locally by the search head. The search headmodifies search query 802 by substituting “stats” (create aggregatestatistics over results sets received from the indexers at the searchhead) with “prestats” (create statistics by the indexer from localresults set) to produce search query 804, and then distributes searchquery 804 to distributed indexers, which are also referred to as “searchpeers.” Note that search queries may generally specify search criteriaor operations to be performed on events that meet the search criteria.Search queries may also specify field names, as well as search criteriafor the values in the fields or operations to be performed on the valuesin the fields. Moreover, the search head may distribute the full searchquery to the search peers as illustrated in FIG. 4 , or mayalternatively distribute a modified version (e.g., a more restrictedversion) of the search query to the search peers. In this example, theindexers are responsible for producing the results and sending them tothe search head. After the indexers return the results to the searchhead, the search head aggregates the received results 806 to form asingle search result set. By executing the query in this manner, thesystem effectively distributes the computational operations across theindexers while minimizing data transfers.

2.10.2. Keyword Index

As described above with reference to the flow charts in FIG. 3 and FIG.4 , data intake and query system 108 can construct and maintain one ormore keyword indices to quickly identify events containing specifickeywords. This technique can greatly speed up the processing of queriesinvolving specific keywords. As mentioned above, to build a keywordindex, an indexer first identifies a set of keywords. Then, the indexerincludes the identified keywords in an index, which associates eachstored keyword with references to events containing that keyword, or tolocations within events where that keyword is located. When an indexersubsequently receives a keyword-based query, the indexer can access thekeyword index to quickly identify events containing the keyword.

2.10.3. High Performance Analytics Store

To speed up certain types of queries, some embodiments of system 108create a high performance analytics store, which is referred to as a“summarization table,” that contains entries for specific field-valuepairs. Each of these entries keeps track of instances of a specificvalue in a specific field in the event data and includes references toevents containing the specific value in the specific field. For example,an example entry in a summarization table can keep track of occurrencesof the value “94107” in a “ZIP code” field of a set of events and theentry includes references to all of the events that contain the value“94107” in the ZIP code field. This optimization technique enables thesystem to quickly process queries that seek to determine how many eventshave a particular value for a particular field. To this end, the systemcan examine the entry in the summarization table to count instances ofthe specific value in the field without having to go through theindividual events or perform data extractions at search time. Also, ifthe system needs to process all events that have a specific field-valuecombination, the system can use the references in the summarizationtable entry to directly access the events to extract further informationwithout having to search all of the events to find the specificfield-value combination at search time.

In some embodiments, the system maintains a separate summarization tablefor each of the above-described time-specific buckets that stores eventsfor a specific time range. A bucket-specific summarization tableincludes entries for specific field-value combinations that occur inevents in the specific bucket. Alternatively, the system can maintain aseparate summarization table for each indexer. The indexer-specificsummarization table includes entries for the events in a data store thatare managed by the specific indexer. Indexer-specific summarizationtables may also be bucket-specific.

The summarization table can be populated by running a periodic querythat scans a set of events to find instances of a specific field-valuecombination, or alternatively instances of all field-value combinationsfor a specific field. A periodic query can be initiated by a user, orcan be scheduled to occur automatically at specific time intervals. Aperiodic query can also be automatically launched in response to a querythat asks for a specific field-value combination.

In some cases, when the summarization tables may not cover all of theevents that are relevant to a query, the system can use thesummarization tables to obtain partial results for the events that arecovered by summarization tables, but may also have to search throughother events that are not covered by the summarization tables to produceadditional results. These additional results can then be combined withthe partial results to produce a final set of results for the query. Thesummarization table and associated techniques are described in moredetail in U.S. Pat. No. 8,682,925, entitled “Distributed HighPerformance Analytics Store”, issued on 25 Mar. 2014, U.S. patentapplication Ser. No. 14/170,159, entitled “SUPPLEMENTING A HIGHPERFORMANCE ANALYTICS STORE WITH EVALUATION OF INDIVIDUAL EVENTS TORESPOND TO AN EVENT QUERY”, filed on 31 Jan. 2014, and U.S. patentapplication Ser. No. 14/815,973, entitled “STORAGE MEDIUM AND CONTROLDEVICE”, filed on 21 Feb. 2014, each of which is hereby incorporated byreference in its entirety.

2.10.4. Accelerating Report Generation

In some embodiments, a data server system such as the SPLUNK® ENTERPRISEsystem can accelerate the process of periodically generating updatedreports based on query results. To accelerate this process, asummarization engine automatically examines the query to determinewhether generation of updated reports can be accelerated by creatingintermediate summaries. If reports can be accelerated, the summarizationengine periodically generates a summary covering data obtained during alatest non-overlapping time period. For example, where the query seeksevents meeting a specified criteria, a summary for the time periodincludes only events within the time period that meet the specifiedcriteria. Similarly, if the query seeks statistics calculated from theevents, such as the number of events that match the specified criteria,then the summary for the time period includes the number of events inthe period that match the specified criteria.

In addition to the creation of the summaries, the summarization engineschedules the periodic updating of the report associated with the query.During each scheduled report update, the query engine determines whetherintermediate summaries have been generated covering portions of the timeperiod covered by the report update. If so, then the report is generatedbased on the information contained in the summaries. Also, if additionalevent data has been received and has not yet been summarized, and isrequired to generate the complete report, the query can be run on thisadditional event data. Then, the results returned by this query on theadditional event data, along with the partial results obtained from theintermediate summaries, can be combined to generate the updated report.This process is repeated each time the report is updated. Alternatively,if the system stores events in buckets covering specific time ranges,then the summaries can be generated on a bucket-by-bucket basis. Notethat producing intermediate summaries can save the work involved inre-running the query for previous time periods, so advantageously onlythe newer event data needs to be processed while generating an updatedreport. These report acceleration techniques are described in moredetail in U.S. Pat. No. 8,589,403, entitled “Compressed Journaling InEvent Tracking Files For Metadata Recovery And Replication”, issued on19 Nov. 2013, U.S. Pat. No. 8,412,696, entitled “Real Time Searching AndReporting”, issued on 2 Apr. 2011, and U.S. Pat. Nos. 8,589,375 and8,589,432, both also entitled “REAL TIME SEARCHING AND REPORTING”, bothissued on 19 Nov. 2013, each of which is hereby incorporated byreference in its entirety.

2.11. Security Features

The SPLUNK® ENTERPRISE platform provides various schemas, dashboards andvisualizations that simplify developers' task to create applicationswith additional capabilities. One such application is the SPLUNK® APPFOR ENTERPRISE SECURITY, which performs monitoring and alertingoperations and includes analytics to facilitate identifying both knownand unknown security threats based on large volumes of data stored bythe SPLUNK® ENTERPRISE system. SPLUNK® APP FOR ENTERPRISE SECURITYprovides the security practitioner with visibility intosecurity-relevant threats found in the enterprise infrastructure bycapturing, monitoring, and reporting on data from enterprise securitydevices, systems, and applications. Through the use of SPLUNK®ENTERPRISE searching and reporting capabilities, SPLUNK® APP FORENTERPRISE SECURITY provides a top-down and bottom-up view of anorganization's security posture.

The SPLUNK® APP FOR ENTERPRISE SECURITY leverages SPLUNK® ENTERPRISEsearch-time normalization techniques, saved searches, and correlationsearches to provide visibility into security-relevant threats andactivity and generate notable events for tracking. The App enables thesecurity practitioner to investigate and explore the data to find new orunknown threats that do not follow signature-based patterns.

Conventional Security Information and Event Management (SIEM) systemsthat lack the infrastructure to effectively store and analyze largevolumes of security-related data. Traditional SIEM systems typically usefixed schemas to extract data from pre-defined security-related fieldsat data ingestion time and storing the extracted data in a relationaldatabase. This traditional data extraction process (and associatedreduction in data size) that occurs at data ingestion time inevitablyhampers future incident investigations that may need original data todetermine the root cause of a security issue, or to detect the onset ofan impending security threat.

In contrast, the SPLUNK® APP FOR ENTERPRISE SECURITY system stores largevolumes of minimally processed security-related data at ingestion timefor later retrieval and analysis at search time when a live securitythreat is being investigated. To facilitate this data retrieval process,the SPLUNK® APP FOR ENTERPRISE SECURITY provides pre-specified schemasfor extracting relevant values from the different types ofsecurity-related event data and enables a user to define such schemas.

The SPLUNK® APP FOR ENTERPRISE SECURITY can process many types ofsecurity-related information. In general, this security-relatedinformation can include any information that can be used to identifysecurity threats. For example, the security-related information caninclude network-related information, such as IP addresses, domain names,asset identifiers, network traffic volume, uniform resource locatorstrings, and source addresses. The process of detecting security threatsfor network-related information is further described in U.S. Pat. No.8,826,434, entitled “SECURITY THREAT DETECTION BASED ON INDICATIONS INBIG DATA OF ACCESS TO NEWLY REGISTERED DOMAINS”, issued on 2 Sep. 2014,U.S. patent application Ser. No. 13/956,252, entitled “INVESTIGATIVE ANDDYNAMIC DETECTION OF POTENTIAL SECURITY-THREAT INDICATORS FROM EVENTS INBIG DATA”, filed on 31 Jul. 2013, U.S. patent application Ser. No.14/445,018, entitled “GRAPHIC DISPLAY OF SECURITY THREATS BASED ONINDICATIONS OF ACCESS TO NEWLY REGISTERED DOMAINS”, filed on 28 Jul.2014, U.S. patent application Ser. No. 14/445,023, entitled “SECURITYTHREAT DETECTION OF NEWLY REGISTERED DOMAINS”, filed on 28 Jul. 2014,U.S. patent application Ser. No. 14/815,971, entitled “SECURITY THREATDETECTION USING DOMAIN NAME ACCESSES”, filed on 1 Aug. 2015, and U.S.patent application Ser. No. 14/815,972, entitled “SECURITY THREATDETECTION USING DOMAIN NAME REGISTRATIONS”, filed on 1 Aug. 2015, eachof which is hereby incorporated by reference in its entirety for allpurposes. Security-related information can also include malwareinfection data and system configuration information, as well as accesscontrol information, such as login/logout information and access failurenotifications. The security-related information can originate fromvarious sources within a data center, such as hosts, virtual machines,storage devices and sensors. The security-related information can alsooriginate from various sources in a network, such as routers, switches,email servers, proxy servers, gateways, firewalls andintrusion-detection systems.

During operation, the SPLUNK® APP FOR ENTERPRISE SECURITY facilitatesdetecting “notable events” that are likely to indicate a securitythreat. These notable events can be detected in a number of ways: (1) auser can notice a correlation in the data and can manually identify acorresponding group of one or more events as “notable;” or (2) a usercan define a “correlation search” specifying criteria for a notableevent, and every time one or more events satisfy the criteria, theapplication can indicate that the one or more events are notable. A usercan alternatively select a pre-defined correlation search provided bythe application. Note that correlation searches can be run continuouslyor at regular intervals (e.g., every hour) to search for notable events.Upon detection, notable events can be stored in a dedicated “notableevents index,” which can be subsequently accessed to generate variousvisualizations containing security-related information. Also, alerts canbe generated to notify system operators when important notable eventsare discovered.

The SPLUNK® APP FOR ENTERPRISE SECURITY provides various visualizationsto aid in discovering security threats, such as a “key indicators view”that enables a user to view security metrics, such as counts ofdifferent types of notable events. For example, FIG. 9A illustrates anexample key indicators view 900 that comprises a dashboard, which candisplay a value 901, for various security-related metrics, such asmalware infections 902. It can also display a change in a metric value903, which indicates that the number of malware infections increased by63 during the preceding interval. Key indicators view 900 additionallydisplays a histogram panel 904 that displays a histogram of notableevents organized by urgency values, and a histogram of notable eventsorganized by time intervals. This key indicators view is described infurther detail in pending U.S. patent application Ser. No. 13/956,338,entitled “Key Indicators View”, filed on 31 Jul. 2013, and which ishereby incorporated by reference in its entirety for all purposes.

These visualizations can also include an “incident review dashboard”that enables a user to view and act on “notable events.” These notableevents can include: (1) a single event of high importance, such as anyactivity from a known web attacker; or (2) multiple events thatcollectively warrant review, such as a large number of authenticationfailures on a host followed by a successful authentication. For example,FIG. 9B illustrates an example incident review dashboard 910 thatincludes a set of incident attribute fields 911 that, for example,enables a user to specify a time range field 912 for the displayedevents. It also includes a timeline 913 that graphically illustrates thenumber of incidents that occurred in time intervals over the selectedtime range. It additionally displays an events list 914 that enables auser to view a list of all of the notable events that match the criteriain the incident attributes fields 911. To facilitate identifyingpatterns among the notable events, each notable event can be associatedwith an urgency value (e.g., low, medium, high, critical), which isindicated in the incident review dashboard. The urgency value for adetected event can be determined based on the severity of the event andthe priority of the system component associated with the event.

2.12. Data Center Monitoring

As mentioned above, the SPLUNK® ENTERPRISE platform provides variousfeatures that simplify the developers's task to create variousapplications. One such application is SPLUNK® APP FOR VMWARE® thatprovides operational visibility into granular performance metrics, logs,tasks and events, and topology from hosts, virtual machines and virtualcenters. It empowers administrators with an accurate real-time pictureof the health of the environment, proactively identifying performanceand capacity bottlenecks.

Conventional data-center-monitoring systems lack the infrastructure toeffectively store and analyze large volumes of machine-generated data,such as performance information and log data obtained from the datacenter. In conventional data-center-monitoring systems,machine-generated data is typically pre-processed prior to being stored,for example, by extracting pre-specified data items and storing them ina database to facilitate subsequent retrieval and analysis at searchtime. However, the rest of the data is not saved and discarded duringpre-processing.

In contrast, the SPLUNK® APP FOR VMWARE® stores large volumes ofminimally processed machine data, such as performance information andlog data, at ingestion time for later retrieval and analysis at searchtime when a live performance issue is being investigated. In addition todata obtained from various log files, this performance-relatedinformation can include values for performance metrics obtained throughan application programming interface (API) provided as part of thevSphere Hypervisor™ system distributed by VMware, Inc. of Palo Alto,Calif. For example, these performance metrics can include: (1)CPU-related performance metrics; (2) disk-related performance metrics;(3) memory-related performance metrics; (4) network-related performancemetrics; (5) energy-usage statistics; (6) data-traffic-relatedperformance metrics; (7) overall system availability performancemetrics; (8) cluster-related performance metrics; and (9) virtualmachine performance statistics. Such performance metrics are describedin U.S. patent application Ser. No. 14/167,316, entitled “CorrelationFor User-Selected Time Ranges Of Values For Performance Metrics OfComponents In An Information-Technology Environment With Log Data FromThat Information-Technology Environment”, filed on 29 Jan. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

To facilitate retrieving information of interest from performance dataand log files, the SPLUNK® APP FOR VMWARE® provides pre-specifiedschemas for extracting relevant values from different types ofperformance-related event data, and also enables a user to define suchschemas.

The SPLUNK® APP FOR VMWARE® additionally provides various visualizationsto facilitate detecting and diagnosing the root cause of performanceproblems. For example, one such visualization is a “proactive monitoringtree” that enables a user to easily view and understand relationshipsamong various factors that affect the performance of a hierarchicallystructured computing system. This proactive monitoring tree enables auser to easily navigate the hierarchy by selectively expanding nodesrepresenting various entities (e.g., virtual centers or computingclusters) to view performance information for lower-level nodesassociated with lower-level entities (e.g., virtual machines or hostsystems). Example node-expansion operations are illustrated in FIG. 9C,wherein nodes 933 and 934 are selectively expanded. Note that nodes931-939 can be displayed using different patterns or colors to representdifferent performance states, such as a critical state, a warning state,a normal state or an unknown/offline state. The ease of navigationprovided by selective expansion in combination with the associatedperformance-state information enables a user to quickly diagnose theroot cause of a performance problem. The proactive monitoring tree isdescribed in further detail in U.S. patent application Ser. No.14/253,490, entitled “PROACTIVE MONITORING TREE WITH SEVERITY STATESORTING”, filed on 15 Apr. 2014, and U.S. patent application Ser. No.14/812,948, also entitled “PROACTIVE MONITORING TREE WITH SEVERITY STATESORTING”, filed on 29 Jul. 2015, each of which is hereby incorporated byreference in its entirety for all purposes.

The SPLUNK® APP FOR VMWARE® also provides a user interface that enablesa user to select a specific time range and then view heterogeneous datacomprising events, log data, and associated performance metrics for theselected time range. For example, the screen illustrated in FIG. 9Ddisplays a listing of recent “tasks and events” and a listing of recent“log entries” for a selected time range above a performance-metric graphfor “average CPU core utilization” for the selected time range. Notethat a user is able to operate pull-down menus 942 to selectivelydisplay different performance metric graphs for the selected time range.This enables the user to correlate trends in the performance-metricgraph with corresponding event and log data to quickly determine theroot cause of a performance problem. This user interface is described inmore detail in U.S. patent application Ser. No. 14/167,316, entitled“Correlation For User-Selected Time Ranges Of Values For PerformanceMetrics Of Components In An Information-Technology Environment With LogData From That Information-Technology ENVIRONMENT”, filed on 29 Jan.2014, and which is hereby incorporated by reference in its entirety forall purposes.

2.13. Cloud-Based System Overview

The example data intake and query system 108 described in reference toFIG. 2 comprises several system components, including one or moreforwarders, indexers, and search heads. In some environments, a user ofa data intake and query system 108 may install and configure, oncomputing devices owned and operated by the user, one or more softwareapplications that implement some or all of these system components. Forexample, a user may install a software application on server computersowned by the user and configure each server to operate as one or more ofa forwarder, an indexer, a search head, etc. This arrangement generallymay be referred to as an “on-premises” solution. That is, the system 108is installed and operates on computing devices directly controlled bythe user of the system. Some users may prefer an on-premises solutionbecause it may provide a greater level of control over the configurationof certain aspects of the system (e.g., security, privacy, standards,controls, etc.). However, other users may instead prefer an arrangementin which the user is not directly responsible for providing and managingthe computing devices upon which various components of system 108operate.

In one embodiment, to provide an alternative to an entirely on-premisesenvironment for system 108, one or more of the components of a dataintake and query system instead may be provided as a cloud-basedservice. In this context, a cloud-based service refers to a servicehosted by one more computing resources that are accessible to end usersover a network, for example, by using a web browser or other applicationon a client device to interface with the remote computing resources. Forexample, a service provider may provide a cloud-based data intake andquery system by managing computing resources configured to implementvarious aspects of the system (e.g., forwarders, indexers, search heads,etc.) and by providing access to the system to end users via a network.Typically, a user may pay a subscription or other fee to use such aservice. Each subscribing user of the cloud-based service may beprovided with an account that enables the user to configure a customizedcloud-based system based on the user's preferences.

FIG. 10 illustrates a block diagram of an example cloud-based dataintake and query system. Similar to the system of FIG. 2 , the networkedcomputer system 1000 includes input data sources 202 and forwarders 204.These input data sources and forwarders may be in a subscriber's privatecomputing environment. Alternatively, they might be directly managed bythe service provider as part of the cloud service. In the example system1000, one or more forwarders 204 and client devices 1002 are coupled toa cloud-based data intake and query system 1006 via one or more networks1004. Network 1004 broadly represents one or more LANs, WANs, cellularnetworks, intranetworks, internetworks, etc., using any of wired,wireless, terrestrial microwave, satellite links, etc., and may includethe public Internet, and is used by client devices 1002 and forwarders204 to access the system 1006. Similar to the system of 108, each of theforwarders 204 may be configured to receive data from an input sourceand to forward the data to other components of the system 1006 forfurther processing.

In an embodiment, a cloud-based data intake and query system 1006 maycomprise a plurality of system instances 1008. In general, each systeminstance 1008 may include one or more computing resources managed by aprovider of the cloud-based system 1006 made available to a particularsubscriber. The computing resources comprising a system instance 1008may, for example, include one or more servers or other devicesconfigured to implement one or more forwarders, indexers, search heads,and other components of a data intake and query system, similar tosystem 108. As indicated above, a subscriber may use a web browser orother application of a client device 1002 to access a web portal orother interface that enables the subscriber to configure an instance1008.

Providing a data intake and query system as described in reference tosystem 108 as a cloud-based service presents a number of challenges.Each of the components of a system 108 (e.g., forwarders, indexers andsearch heads) may at times refer to various configuration files storedlocally at each component. These configuration files typically mayinvolve some level of user configuration to accommodate particular typesof data a user desires to analyze and to account for other userpreferences. However, in a cloud-based service context, users typicallymay not have direct access to the underlying computing resourcesimplementing the various system components (e.g., the computingresources comprising each system instance 1008) and may desire to makesuch configurations indirectly, for example, using one or more web-basedinterfaces. Thus, the techniques and systems described herein forproviding user interfaces that enable a user to configure source typedefinitions are applicable to both on-premises and cloud-based servicecontexts, or some combination thereof (e.g., a hybrid system where bothan on-premises environment such as SPLUNK® ENTERPRISE and a cloud-basedenvironment such as SPLUNK CLOUD™ are centrally visible).

2.14. Searching Externally Archived Data

FIG. 11 shows a block diagram of an example of a data intake and querysystem 108 that provides transparent search facilities for data systemsthat are external to the data intake and query system. Such facilitiesare available in the HUNK® system provided by Splunk Inc. of SanFrancisco, Calif. HUNK® represents an analytics platform that enablesbusiness and IT teams to rapidly explore, analyze, and visualize data inHadoop and NoSQL data stores.

The search head 210 of the data intake and query system receives searchrequests from one or more client devices 1104 over network connections1120. As discussed above, the data intake and query system 108 mayreside in an enterprise location, in the cloud, etc. FIG. 11 illustratesthat multiple client devices 1104 a, 1104 b, . . . , 1104 n maycommunicate with the data intake and query system 108. The clientdevices 1104 may communicate with the data intake and query system usinga variety of connections. For example, one client device in FIG. 11 isillustrated as communicating over an Internet (Web) protocol, anotherclient device is illustrated as communicating via a command lineinterface, and another client device is illustrated as communicating viaa system developer kit (SDK).

The search head 210 analyzes the received search request to identifyrequest parameters. If a search request received from one of the clientdevices 1104 references an index maintained by the data intake and querysystem, then the search head 210 connects to one or more indexers 206 ofthe data intake and query system for the index referenced in the requestparameters. That is, if the request parameters of the search requestreference an index, then the search head accesses the data in the indexvia the indexer. The data intake and query system 108 may include one ormore indexers 206, depending on system access resources andrequirements. As described further below, the indexers 206 retrieve datafrom their respective local data stores 208 as specified in the searchrequest. The indexers and their respective data stores can comprise oneor more storage devices and typically reside on the same system, thoughthey may be connected via a local network connection.

If the request parameters of the received search request reference anexternal data collection, which is not accessible to the indexers 206 orunder the management of the data intake and query system, then thesearch head 210 can access the external data collection through anExternal Result Provider (ERP) process 1110. An external data collectionmay be referred to as a “virtual index” (plural, “virtual indices”). AnERP process provides an interface through which the search head 210 mayaccess virtual indices.

Thus, a search reference to an index of the system relates to a locallystored and managed data collection. In contrast, a search reference to avirtual index relates to an externally stored and managed datacollection, which the search head may access through one or more ERPprocesses 1110, 1112. FIG. 11 shows two ERP processes 1110, 1112 thatconnect to respective remote (external) virtual indices, which areindicated as a Hadoop or another system 1114 (e.g., Amazon S3, AmazonEMR, other Hadoop Compatible File Systems (HCFS), etc.) and a relationaldatabase management system (RDBMS) 1116. Other virtual indices mayinclude other file organizations and protocols, such as Structured QueryLanguage (SQL) and the like. The ellipses between the ERP processes1110, 1112 indicate optional additional ERP processes of the data intakeand query system 108. An ERP process may be a computer process that isinitiated or spawned by the search head 210 and is executed by thesearch data intake and query system 108. Alternatively or additionally,an ERP process may be a process spawned by the search head 210 on thesame or different host system as the search head 210 resides.

The search head 210 may spawn a single ERP process in response tomultiple virtual indices referenced in a search request, or the searchhead may spawn different ERP processes for different virtual indices.Generally, virtual indices that share common data configurations orprotocols may share ERP processes. For example, all search queryreferences to a Hadoop file system may be processed by the same ERPprocess, if the ERP process is suitably configured. Likewise, all searchquery references to an SQL database may be processed by the same ERPprocess. In addition, the search head may provide a common ERP processfor common external data source types (e.g., a common vendor may utilizea common ERP process, even if the vendor includes different data storagesystem types, such as Hadoop and SQL). Common indexing schemes also maybe handled by common ERP processes, such as flat text files or Weblogfiles.

The search head 210 determines the number of ERP processes to beinitiated via the use of configuration parameters that are included in asearch request message. Generally, there is a one-to-many relationshipbetween an external results provider “family” and ERP processes. Thereis also a one-to-many relationship between an ERP process andcorresponding virtual indices that are referred to in a search request.For example, using RDBMS, assume two independent instances of such asystem by one vendor, such as one RDBMS for production and another RDBMSused for development. In such a situation, it is likely preferable (butoptional) to use two ERP processes to maintain the independent operationas between production and development data. Both of the ERPs, however,will belong to the same family, because the two RDBMS system types arefrom the same vendor.

The ERP processes 1110, 1112 receive a search request from the searchhead 210. The search head may optimize the received search request forexecution at the respective external virtual index. Alternatively, theERP process may receive a search request as a result of analysisperformed by the search head or by a different system process. The ERPprocesses 1110, 1112 can communicate with the search head 210 viaconventional input/output routines (e.g., standard in/standard out,etc.). In this way, the ERP process receives the search request from aclient device such that the search request may be efficiently executedat the corresponding external virtual index.

The ERP processes 1110, 1112 may be implemented as a process of the dataintake and query system. Each ERP process may be provided by the dataintake and query system, or may be provided by process or applicationproviders who are independent of the data intake and query system. Eachrespective ERP process may include an interface application installed ata computer of the external result provider that ensures propercommunication between the search support system and the external resultprovider. The ERP processes 1110, 1112 generate appropriate searchrequests in the protocol and syntax of the respective virtual indices1114, 1116, each of which corresponds to the search request received bythe search head 210. Upon receiving search results from theircorresponding virtual indices, the respective ERP process passes theresult to the search head 210, which may return or display the resultsor a processed set of results based on the returned results to therespective client device.

Client devices 1104 may communicate with the data intake and querysystem 108 through a network interface 1120, e.g., one or more LANs,WANs, cellular networks, intranetworks, and/or internetworks using anyof wired, wireless, terrestrial microwave, satellite links, etc., andmay include the public Internet.

The analytics platform utilizing the External Result Provider processdescribed in more detail in U.S. Pat. No. 8,738,629, entitled “ExternalResult Provided Process For Retrieving Data Stored Using A DifferentConfiguration Or Protocol”, issued on 27 May 2014, U.S. Pat. No.8,738,587, entitled “PROCESSING A SYSTEM SEARCH REQUEST BY RETRIEVINGRESULTS FROM BOTH A NATIVE INDEX AND A VIRTUAL INDEX”, issued on 25 Jul.2013, U.S. patent application Ser. No. 14/266,832, entitled “PROCESSINGA SYSTEM SEARCH REQUEST ACROSS DISPARATE DATA COLLECTION SYSTEMS”, filedon 1 May 2014, and U.S. patent application Ser. No. 14/449,144, entitled“PROCESSING A SYSTEM SEARCH REQUEST INCLUDING EXTERNAL DATA SOURCES”,filed on 31 Jul. 2014, each of which is hereby incorporated by referencein its entirety for all purposes.

2.14.1. ERP Process Features

The ERP processes described above may include two operation modes: astreaming mode and a reporting mode. The ERP processes can operate instreaming mode only, in reporting mode only, or in both modessimultaneously. Operating in both modes simultaneously is referred to asmixed mode operation. In a mixed mode operation, the ERP at some pointcan stop providing the search head with streaming results and onlyprovide reporting results thereafter, or the search head at some pointmay start ignoring streaming results it has been using and only usereporting results thereafter.

The streaming mode returns search results in real time, with minimalprocessing, in response to the search request. The reporting modeprovides results of a search request with processing of the searchresults prior to providing them to the requesting search head, which inturn provides results to the requesting client device. ERP operationwith such multiple modes provides greater performance flexibility withregard to report time, search latency, and resource utilization.

In a mixed mode operation, both streaming mode and reporting mode areoperating simultaneously. The streaming mode results (e.g., the raw dataobtained from the external data source) are provided to the search head,which can then process the results data (e.g., break the raw data intoevents, timestamp it, filter it, etc.) and integrate the results datawith the results data from other external data sources, and/or from datastores of the search head. The search head performs such processing andcan immediately start returning interim (streaming mode) results to theuser at the requesting client device; simultaneously, the search head iswaiting for the ERP process to process the data it is retrieving fromthe external data source as a result of the concurrently executingreporting mode.

In some instances, the ERP process initially operates in a mixed mode,such that the streaming mode operates to enable the ERP quickly toreturn interim results (e.g., some of the raw or unprocessed datanecessary to respond to a search request) to the search head, enablingthe search head to process the interim results and begin providing tothe client or search requester interim results that are responsive tothe query. Meanwhile, in this mixed mode, the ERP also operatesconcurrently in reporting mode, processing portions of raw data in amanner responsive to the search query. Upon determining that it hasresults from the reporting mode available to return to the search head,the ERP may halt processing in the mixed mode at that time (or somelater time) by stopping the return of data in streaming mode to thesearch head and switching to reporting mode only. The ERP at this pointstarts sending interim results in reporting mode to the search head,which in turn may then present this processed data responsive to thesearch request to the client or search requester. Typically the searchhead switches from using results from the ERP's streaming mode ofoperation to results from the ERP's reporting mode of operation when thehigher bandwidth results from the reporting mode outstrip the amount ofdata processed by the search head in the streaming mode of ERPoperation.

A reporting mode may have a higher bandwidth because the ERP does nothave to spend time transferring data to the search head for processingall the raw data. In addition, the ERP may optionally direct anotherprocessor to do the processing.

The streaming mode of operation does not need to be stopped to gain thehigher bandwidth benefits of a reporting mode; the search head couldsimply stop using the streaming mode results—and start using thereporting mode results—when the bandwidth of the reporting mode hascaught up with or exceeded the amount of bandwidth provided by thestreaming mode. Thus, a variety of triggers and ways to accomplish asearch head's switch from using streaming mode results to usingreporting mode results may be appreciated by one skilled in the art.

The reporting mode can involve the ERP process (or an external system)performing event breaking, time stamping, filtering of events to matchthe search query request, and calculating statistics on the results. Theuser can request particular types of data, such as if the search queryitself involves types of events, or the search request may ask forstatistics on data, such as on events that meet the search request. Ineither case, the search head understands the query language used in thereceived query request, which may be a proprietary language. Oneexemplary query language is Splunk Processing Language (SPL) developedby the assignee of the application, Splunk Inc. The search headtypically understands how to use that language to obtain data from theindexers, which store data in a format used by the SPLUNK® Enterprisesystem.

The ERP processes support the search head, as the search head is notordinarily configured to understand the format in which data is storedin external data sources such as Hadoop or SQL data systems. Rather, theERP process performs that translation from the query submitted in thesearch support system's native format (e.g., SPL if SPLUNK® ENTERPRISEis used as the search support system) to a search query request formatthat will be accepted by the corresponding external data system. Theexternal data system typically stores data in a different format fromthat of the search support system's native index format, and it utilizesa different query language (e.g., SQL or MapReduce, rather than SPL orthe like).

As noted, the ERP process can operate in the streaming mode alone. Afterthe ERP process has performed the translation of the query request andreceived raw results from the streaming mode, the search head canintegrate the returned data with any data obtained from local datasources (e.g., native to the search support system), other external datasources, and other ERP processes (if such operations were required tosatisfy the terms of the search query). An advantage of mixed modeoperation is that, in addition to streaming mode, the ERP process isalso executing concurrently in reporting mode. Thus, the ERP process(rather than the search head) is processing query results (e.g.,performing event breaking, timestamping, filtering, possibly calculatingstatistics if required to be responsive to the search query request,etc.). It should be apparent to those skilled in the art that additionaltime is needed for the ERP process to perform the processing in such aconfiguration. Therefore, the streaming mode will allow the search headto start returning interim results to the user at the client devicebefore the ERP process can complete sufficient processing to startreturning any search results. The switchover between streaming andreporting mode happens when the ERP process determines that theswitchover is appropriate, such as when the ERP process determines itcan begin returning meaningful results from its reporting mode.

The operation described above illustrates the source of operationallatency: streaming mode has low latency (immediate results) and usuallyhas relatively low bandwidth (fewer results can be returned per unit oftime). In contrast, the concurrently running reporting mode hasrelatively high latency (it has to perform a lot more processing beforereturning any results) and usually has relatively high bandwidth (moreresults can be processed per unit of time). For example, when the ERPprocess does begin returning report results, it returns more processedresults than in the streaming mode, because, e.g., statistics only needto be calculated to be responsive to the search request. That is, theERP process doesn't have to take time to first return raw data to thesearch head. As noted, the ERP process could be configured to operate instreaming mode alone and return just the raw data for the search head toprocess in a way that is responsive to the search request.Alternatively, the ERP process can be configured to operate in thereporting mode only. Also, the ERP process can be configured to operatein streaming mode and reporting mode concurrently, as described, withthe ERP process stopping the transmission of streaming results to thesearch head when the concurrently running reporting mode has caught upand started providing results. The reporting mode does not require theprocessing of all raw data that is responsive to the search queryrequest before the ERP process starts returning results; rather, thereporting mode usually performs processing of chunks of events andreturns the processing results to the search head for each chunk.

For example, an ERP process can be configured to merely return thecontents of a search result file verbatim, with little or no processingof results. That way, the search head performs all processing (such asparsing byte streams into events, filtering, etc.). The ERP process canbe configured to perform additional intelligence, such as analyzing thesearch request and handling all the computation that a native searchindexer process would otherwise perform. In this way, the configured ERPprocess provides greater flexibility in features while operatingaccording to desired preferences, such as response latency and resourcerequirements.

2.15. It Service Monitoring

As previously mentioned, the SPLUNK® ENTERPRISE platform providesvarious schemas, dashboards and visualizations that make it easy fordevelopers to create applications to provide additional capabilities.One such application is SPLUNK® IT SERVICE INTELLIGENCE™, which performsmonitoring and alerting operations. It also includes analytics to helpan analyst diagnose the root cause of performance problems based onlarge volumes of data stored by the SPLUNK® ENTERPRISE system ascorrelated to the various services an IT organization provides (aservice-centric view). This differs significantly from conventional ITmonitoring systems that lack the infrastructure to effectively store andanalyze large volumes of service-related event data. Traditional servicemonitoring systems typically use fixed schemas to extract data frompre-defined fields at data ingestion time, wherein the extracted data istypically stored in a relational database. This data extraction processand associated reduction in data content that occurs at data ingestiontime inevitably hampers future investigations, when all of the originaldata may be needed to determine the root cause of or contributingfactors to a service issue.

In contrast, a SPLUNK® IT SERVICE INTELLIGENCE™ system stores largevolumes of minimally-processed service-related data at ingestion timefor later retrieval and analysis at search time, to perform regularmonitoring, or to investigate a service issue. To facilitate this dataretrieval process, SPLUNK® IT SERVICE INTELLIGENCE™ enables a user todefine an IT operations infrastructure from the perspective of theservices it provides. In this service-centric approach, a service suchas corporate e-mail may be defined in terms of the entities employed toprovide the service, such as host machines and network devices. Eachentity is defined to include information for identifying all of theevent data that pertains to the entity, whether produced by the entityitself or by another machine, and considering the many various ways theentity may be identified in raw machine data (such as by a URL, an IPaddress, or machine name). The service and entity definitions canorganize event data around a service so that all of the event datapertaining to that service can be easily identified. This capabilityprovides a foundation for the implementation of Key PerformanceIndicators.

One or more Key Performance Indicators (KPI's) are defined for a servicewithin the SPLUNK® IT SERVICE INTELLIGENCE™ application. Each KPImeasures an aspect of service performance at a point in time or over aperiod of time (aspect KPI's). Each KPI is defined by a search querythat derives a KPI value from the machine data of events associated withthe entities that provide the service. Information in the entitydefinitions may be used to identify the appropriate events at the time aKPI is defined or whenever a KPI value is being determined. The KPIvalues derived over time may be stored to build a valuable repository ofcurrent and historical performance information for the service, and therepository, itself, may be subject to search query processing. AggregateKPIs may be defined to provide a measure of service performancecalculated from a set of service aspect KPI values; this aggregate mayeven be taken across defined timeframes and/or across multiple services.A particular service may have an aggregate KPI derived fromsubstantially all of the aspect KPI's of the service to indicate anoverall health score for the service.

SPLUNK® IT SERVICE INTELLIGENCE™ facilitates the production ofmeaningful aggregate KPI's through a system of KPI thresholds and statevalues. Different KPI definitions may produce values in differentranges, and so the same value may mean something very different from oneKPI definition to another. To address this, SPLUNK® IT SERVICEINTELLIGENCE™ implements a translation of individual KPI values to acommon domain of “state” values. For example, a KPI range of values maybe 1-100, or 50-275, while values in the state domain may be ‘critical,’‘warning,’ ‘normal,’ and ‘informational’. Thresholds associated with aparticular KPI definition determine ranges of values for that KPI thatcorrespond to the various state values. In one case, KPI values 95-100may be set to correspond to ‘critical’ in the state domain. KPI valuesfrom disparate KPI's can be processed uniformly once they are translatedinto the common state values using the thresholds. For example, “normal80% of the time” can be applied across various KPI's. To providemeaningful aggregate KPI's, a weighting value can be assigned to eachKPI so that its influence on the calculated aggregate KPI value isincreased or decreased relative to the other KPI's.

One service in an IT environment often impacts, or is impacted by,another service. SPLUNK® IT SERVICE INTELLIGENCE™ can reflect thesedependencies. For example, a dependency relationship between a corporatee-mail service and a centralized authentication service can be reflectedby recording an association between their respective servicedefinitions. The recorded associations establish a service dependencytopology that informs the data or selection options presented in a GUI,for example. (The service dependency topology is like a “map” showinghow services are connected based on their dependencies.) The servicetopology may itself be depicted in a GUI and may be interactive to allownavigation among related services.

Entity definitions in SPLUNK® IT SERVICE INTELLIGENCE™ can includeinformational fields that can serve as metadata, implied data fields, orattributed data fields for the events identified by other aspects of theentity definition. Entity definitions in SPLUNK® IT SERVICEINTELLIGENCE™ can also be created and updated by an import of tabulardata (as represented in a CSV, another delimited file, or a search queryresult set). The import may be GUI-mediated or processed using importparameters from a GUI-based import definition process. Entitydefinitions in SPLUNK® IT SERVICE INTELLIGENCE™ can also be associatedwith a service by means of a service definition rule. Processing therule results in the matching entity definitions being associated withthe service definition. The rule can be processed at creation time, andthereafter on a scheduled or on-demand basis. This allows dynamic,rule-based updates to the service definition.

During operation, SPLUNK® IT SERVICE INTELLIGENCE™ can recognizeso-called “notable events” that may indicate a service performanceproblem or other situation of interest. These notable events can berecognized by a “correlation search” specifying trigger criteria for anotable event: every time KPI values satisfy the criteria, theapplication indicates a notable event. A severity level for the notableevent may also be specified. Furthermore, when trigger criteria aresatisfied, the correlation search may additionally or alternativelycause a service ticket to be created in an IT service management (ITSM)system, such as a systems available from ServiceNow, Inc., of SantaClara, Calif.

SPLUNK® IT SERVICE INTELLIGENCE™ provides various visualizations builton its service-centric organization of event data and the KPI valuesgenerated and collected. Visualizations can be particularly useful formonitoring or investigating service performance. SPLUNK® IT SERVICEINTELLIGENCE™ provides a service monitoring interface suitable as thehome page for ongoing IT service monitoring. The interface isappropriate for settings such as desktop use or for a wall-mounteddisplay in a network operations center (NOC). The interface mayprominently display a services health section with tiles for theaggregate KPI's indicating overall health for defined services and ageneral KPI section with tiles for KPI's related to individual serviceaspects. These tiles may display KPI information in a variety of ways,such as by being colored and ordered according to factors like the KPIstate value. They also can be interactive and navigate to visualizationsof more detailed KPI information.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a service-monitoring dashboardvisualization based on a user-defined template. The template can includeuser-selectable widgets of varying types and styles to display KPIinformation. The content and the appearance of widgets can responddynamically to changing KPI information. The KPI widgets can appear inconjunction with a background image, user drawing objects, or othervisual elements, that depict the IT operations environment, for example.The KPI widgets or other GUI elements can be interactive so as toprovide navigation to visualizations of more detailed KPI information.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a visualization showingdetailed time-series information for multiple KPI's in parallel graphlanes. The length of each lane can correspond to a uniform time range,while the width of each lane may be automatically adjusted to fit thedisplayed KPI data. Data within each lane may be displayed in a userselectable style, such as a line, area, or bar chart. During operation auser may select a position in the time range of the graph lanes toactivate lane inspection at that point in time. Lane inspection maydisplay an indicator for the selected time across the graph lanes anddisplay the KPI value associated with that point in time for each of thegraph lanes. The visualization may also provide navigation to aninterface for defining a correlation search, using information from thevisualization to pre-populate the definition.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a visualization for incidentreview showing detailed information for notable events. The incidentreview visualization may also show summary information for the notableevents over a time frame, such as an indication of the number of notableevents at each of a number of severity levels. The severity leveldisplay may be presented as a rainbow chart with the warmest colorassociated with the highest severity classification. The incident reviewvisualization may also show summary information for the notable eventsover a time frame, such as the number of notable events occurring withinsegments of the time frame. The incident review visualization maydisplay a list of notable events within the time frame ordered by anynumber of factors, such as time or severity. The selection of aparticular notable event from the list may display detailed informationabout that notable event, including an identification of the correlationsearch that generated the notable event.

SPLUNK® IT SERVICE INTELLIGENCE™ provides pre-specified schemas forextracting relevant values from the different types of service-relatedevent data. It also enables a user to define such schemas.

3.0. Skewing Scheduled Search Queries

FIG. 18 illustrates a block diagram of an example data intake and querysystem 1808 that includes a search query scheduling system 1810 andmultiple search heads 210 in accordance with the disclosed embodiments.As shown, the data intake and query system 1808 includes, withoutlimitation, search heads 210, indexers 206, and a search queryscheduling system 1810 that communicate with each other over a network1804. Each of the indexers 206 includes, without limitation, a datastore 208. The search heads 210, indexers 206, and data stores 208function substantially the same as corresponding elements of the dataintake and query system 108 of FIG. 2 except as further described below.Network 1804 broadly represents one or more LANs, WANs, cellularnetworks (e.g., LTE, HSPA, 3G, and other cellular technologies), and/ornetworks using any of wired, wireless, terrestrial microwave, orsatellite links, and may include the public Internet.

Each search head 210 of data intake and query system 1808 receives oneor more search queries via search query scheduling system 1810. Eachsearch head 210 analyzes each search query to determine what portion(s)of the search query can be delegated to indexers 206 and what portionsof the search query can be executed locally by the search head 210. Eachsearch head 210 distributes the determined portions of the search queryto the appropriate indexers 206. Each search head 210 coordinates withpeer search heads and with search query scheduling system 1810 toschedule jobs, replicate search results, update configurations, fulfillsearch requests, etc.

As further described herein, search query scheduling system 1810schedules search queries for execution by search heads 210 and by searchquery scheduling system 1810. Search query scheduling system 1810communicates with search heads 210 to dispatch each search query to oneor more of the search heads 210 for execution. In operation, searchquery scheduling system 1810 and search heads 210 direct network trafficassociated with search queries to indexers 206 via network 1804.Indexers 206, in turn, return network traffic associated with searchresults to search query scheduling system 1810 and search heads 210 vianetwork 1804.

In some embodiments, search query scheduling system 1810 may be aninstance of a search head 210. In such embodiments, each search head 210may be capable of performing the functions of search query schedulingsystem 1810. Accordingly, each search head 210 may maintain a copy ofthe list of all search queries scheduled to execute within data intakeand query system 1808. If the functions of search query schedulingsystem 1810 are to be transferred to a different search head 210, thenthe search head 210 may become the new search query scheduling system1810 and begin to schedule search queries for execution by the searchheads 210. Likewise, the current search query scheduling system 1810 maybecome one of the search heads 210 and cease to schedule search queriesfor execution by the other search heads 210. The current search queryscheduling system 1810 is now described in further detail.

FIG. 19 is a more detailed illustration of the search query schedulingsystem 1810 of FIG. 18 in accordance with the disclosed embodiments. Asshown, the search query scheduling system 1810 includes, withoutlimitation, a processor 1902, storage 1904, an input/output (I/O) deviceinterface 1906, a network interface 1908, an interconnect 1910, and asystem memory 1912.

In general, processor 1902 retrieves and executes programminginstructions stored in system memory 1912. Processor 1902 may be anytechnically feasible form of processing device configured to processdata and execute program code. Processor 1902 could be, for example, acentral processing unit (CPU), a graphics processing unit (GPU), anapplication-specific integrated circuit (ASIC), a field-programmablegate array (FPGA), and so forth. Processor 1902 stores and retrievesapplication data residing in the system memory 1912. Processor 1902 isincluded to be representative of a single CPU, multiple CPUs, a singleCPU having multiple processing cores, and the like. In operation,processor 1902 is the master processor of search query scheduling system1810, controlling and coordinating operations of other systemcomponents. System memory 1912 stores software applications and data foruse by processor 1902. Processor 1902 executes software applicationsstored within system memory 1912 and optionally an operating system. Inparticular, processor 1902 executes software and then performs one ormore of the functions and operations set forth in the presentapplication.

The storage 1904 may be a disk drive storage device. Although shown as asingle unit, the storage 1904 may be a combination of fixed and/orremovable storage devices, such as fixed disc drives, floppy discdrives, tape drives, removable memory cards, or optical storage, networkattached storage (NAS), or a storage area-network (SAN). Processor 1902communicates to other computing devices and systems via networkinterface 1908, where network interface 1908 is configured to transmitand receive data via a communications network.

The interconnect 1910 facilitates transmission, such as of programminginstructions and application data, between the processor 1902,input/output (I/O) devices interface 1906, storage 1904, networkinterface 1908, and system memory 1912. The I/O devices interface 1906is configured to receive input data from user I/O devices 1922. Examplesof user I/O devices 1922 may include one of more buttons, a keyboard,and a mouse or other pointing device. The I/O devices interface 1906 mayalso include an audio output unit configured to generate an electricalaudio output signal, and user I/O devices 1922 may further include aspeaker configured to generate an acoustic output in response to theelectrical audio output signal. Another example of a user I/O device1922 is a display device that generally represents any technicallyfeasible means for generating an image for display. For example, thedisplay device may be a liquid crystal display (LCD) display, CRTdisplay, or DLP display. The display device may be a TV that includes abroadcast or cable tuner for receiving digital or analog televisionsignals.

The system memory 1912 includes, without limitation, a search queryscheduling program 1930, and a data store 1940. In operation, processor1902 executes search query scheduling program 1930 to perform one ormore of the techniques disclosed herein.

Search query scheduling program 1930 determines whether or not searchquery skewing is enabled. If search query skewing is not enabled, thensearch query scheduling program 1930 does not skew the scheduling of anysearch queries. If, on the other hand, search query skewing is enabled,then search query scheduling program 1930 computes a skew value for eachsearch query that is eligible for skewing. The skew value for aparticular search query is the amount of time the execution of theparticular search query is delayed from the scheduled execution time forthe search query. For example, four search queries that are scheduled toexecute every minute could have skew values of 0 seconds, 15 seconds, 30seconds and 45 seconds, respectively. In this example, the executiontime of the four search queries would be skewed to the beginning of eachminute, 15 seconds after the beginning of each minute, 30 seconds afterthe beginning of each minute, and 45 seconds after the beginning of eachminute, respectively.

In addition, the skew value for each search query is subject to amaximum skew value, which, in general, may not be exceeded. Typically,the maximum skew value for a given search query is based on thechronographic schedule string (referred to herein as a “cron schedulestring”). The cron schedule string determines how often a correspondingsearch query is scheduled to execute. For example, four cron schedulestrings could specify that four corresponding search queries would bescheduled to execute once every minute, once every ten minutes, onceevery hour, or once every four hours. Correspondingly, the maximumallowable skew for these four search queries, based on these cronschedule strings, would be one minute, ten minutes, one hour, and fourhours, respectively. The skew values for these four search queries wouldbe limited so as not to exceed these respective maximum skew values.

In some embodiments, the maximum allowable skew may be further based onan allowable skew amount (referred to herein as an “allow skewsetting”). The allow skew setting may be expressed as a percentage or asa specific duration of time. If the allow skew setting is expressed as apercentage, then the maximum allowable skew may be based on the cronschedule string multiplied by the percentage specified by the allow skewsetting. In one example, the allow skew setting could be specified as75%. The maximum allowable skew for four given search queries based onthe cron schedule strings could be one minute, ten minutes, one hour,and four hours, respectively. The modified maximum allowable skew forthese four given search queries would then be 45 seconds, 7.5 minutes,45 minutes, and three hours, respectively. Alternatively, the allow skewsetting may be expressed as a specific duration of time, such as fiveminutes or one hour. In such embodiments, the maximum allowable skew maybe set to the duration of time specified by the allow skew setting, fiveminutes or one hour in this example, regardless of the cron schedulestring. Finally, if the allow skew setting is set to zero (‘0’), thenskewing is disabled for the corresponding search queries. An allow skewsetting of 0 is a special case, where an allow skew setting of 0 doesnot need to be expressed as a percentage or with some other unitspecifier that specifies a fixed duration.

In some embodiments, search query scheduling program 1930 may determinea maximum allowable skew based on both a global allow skew setting andper search query allow skew settings. In such embodiments, if the globalallow skew setting is set to 0, then search query skewing may beglobally disabled for all search queries. If, on the other hand, theglobal allow skew setting is set to any other valid setting, then searchquery skewing may be globally enabled for all eligible search queries.The final skew value for each search query may be computed by thetechniques described herein. If the allow skew setting for a particularsearch query is set to 0, then the search query may be consideredineligible for search query skewing. Such a search query is not subjectto search query skewing, even if the global allow skew setting is set toallow search query skewing.

Search query scheduling program 1930 also computes a hash value for thesearch query by applying a hash function to certain parametersassociated with the search query. In some embodiments, the hash functionmay be computed as a function of any one or more of the name or title ofthe search, the textual description of the search, and the search stringthat specifies the parameters of the search. These three fields have arelatively high likelihood of being unique between one search query andanother search query, but a relatively low likelihood of changing overtime. Further, search query scheduling system 1810 and each search head210 maintain a list of current search queries. Consequently, if thetasks of search query scheduling system 1810 are assigned to anothersearch head 210, then the search head 210 can compute the same hashvalues for the search queries as computed by the previous search queryscheduling system 1810.

After the maximum allowable skew and hash value are computed for eachsearch query, search query scheduling program 1930 computes a final skewvalue for each search query. In some embodiments, search queryscheduling program 1930 computes the final skew value according toEquation 1 below:final skew value=hash value mod maximum allowable skew  (Eqn. 1)where “mod” represents the modulo operation. One potential issue withemploying the modulo operation to compute the final skew value is thatthe modulo operation exhibits a bias toward smaller numbers when thehash value is not evenly divisible by the maximum allowable skew.However, this bias effect can be minimized by choosing a hash functionthat produces hash values with a sufficiently high range. For example, asuitable hash function that minimizes this bias effect could producehash values in the range of 0 to 2⁶⁴−1. With the disclosed approach,search query scheduling system 1810 and each search head 210 produce thesame hash values, and consequently the same final skew values, for a setof search queries. As a result, search query scheduling system 1810 andthe search heads 210 skew a particular search query by the same skewvalue without having to transfer skew values or other data betweensearch query scheduling system 1810 and the search heads 210. As aresult, the time between one execution of a particular search query anda consecutive execution of the same search query remains substantiallythe same, even when the tasks performed by the current search queryscheduling system 1810 are transferred to a new search head 210.

In some embodiments, search query scheduling program 1930 may determinea skew tolerance based on the format of the corresponding cron schedulestring. In such embodiments, search query scheduling program 1930 maydetermine that a search query has a relatively high skew tolerance ifthe corresponding cron schedule string corresponds to one of aparticular set of formats. As described herein, search query schedulingprogram 1930 may determine that a search query has a relatively highsearch tolerance if the corresponding cron schedule string correspondsto one of five particular formats. These five formats may respectivelyindicate that the search query is scheduled to execute once everyminute, once every N minutes, once every hour, once every N hours, oronce every day, where N is an integer greater than zero. In someembodiments, the number of particular formats associated with a highskew tolerance may be less than five or greater than five, within thescope of the present invention.

Search query scheduling program 1930 may determine that a search queryhas a relatively low skew tolerance if the corresponding cron schedulestring does not correspond to one of the five (or other number of)particular formats. For example, search query scheduling program 1930could determine that a search query has a relatively low skew toleranceif the corresponding cron schedule string indicates that the searchquery is scheduled to execute at a specific time, such as the 11thminute of each hour or at 10:35 PM each day.

If a search query is determined to have a relatively high skewtolerance, then search query scheduling program 1930 may determine thefinal skew value for the search query based on the maximum allowableskew, as further described herein. If a search query is determined tohave a relatively low skew tolerance, then search query schedulingprogram 1930 may determine the final skew value for the search querybased on a fixed maximum allowable skew of relatively short duration.For example, search query scheduling program 1930 could determine thefinal skew value for a search query has a relatively low skew tolerancebased on a fixed maximum allowable skew of sixty seconds. In thismanner, search query scheduling program 1930 infers whether a particularsearch query has a relatively high skew tolerance or a relatively lowsearch based on format of the cron schedule string corresponding to thesearch query.

In some embodiments, search query scheduling program 1930 may furtheremploy a schedule windows technique, whereby a search query may beassigned a value that conveys to search query scheduling program 1930that the search query is of lesser priority. In general, a schedulewindow provides an indication of how long the corresponding search querymay be delayed. Search queries that do not include a schedule window arepresumed to be of high priority and should execute on time during eachperiod. Search query scheduling program 1930 may employ schedule windowsto help ensure that search queries of greater priority have a betterchance of executing on time and during each period, as specified by thecorresponding cron schedule string. If, during a particular period, dataintake and query system 1808 is resource-constrained to the point ofbeing unable to execute all scheduled search queries to completion, thensearch query scheduling program 1930 may employ schedule windows toincrease the likelihood that search queries of greater priority executeon time. As a result, search queries of lesser priority may not executeon time during a resource-constrained period.

By contrast, search query skewing, as described herein, distributes thedispatch and execution of equally important search queries out over aperiod of time, such as a minute or an hour. As a result, a large set ofsearch queries is less likely to exhibit peak demand for systemresources that temporarily overwhelms one or more components of dataintake and query system 1808.

Search windows and search query skewing are independent features thatmay be employed separately or in conjunction with one another. When bothsearch windows and search query skewing are concurrently employed,search queries are first skewed over a period, as further describedherein. After skewing, those search queries that are scheduled toexecute simultaneously are subject to any corresponding schedule windowsrelated to those search queries. For example, if a large number ofsearch queries are scheduled to execute at the 42nd second of aparticular minute, then those windows with defined schedule windowswould be considered to have lesser priority than search queries withoutdefined search windows.

In one example, a first search query could have a schedule window set tozero. This first search query would be considered to not have a definedsearch window. Therefore, the search query would be considered to be ofrelatively greater priority and would be scheduled according to thesearch query skewing techniques described herein.

A second search query could have a schedule window set to ten minutesand be scheduled to execute once per minute. The second search querywould be considered to be of lesser priority. This second search querywould be scheduled to execute once per minute, but would be a candidateto defer execution if data intake and query system 1808 is unable toexecute all search queries during a particular period. The second searchquery could be deferred additional periods until the second search queryhas been deferred for a duration of time equal to the schedule window.If the second search query has been deferred for a duration of timeequal to the schedule window, then the schedule window of the secondsearch query is temporarily set to 0 in order to increase the likelihoodof execution during the next period.

Finally, a third search query could have a schedule window set to autoor automatic. The third search query would be considered to be of lesserpriority. The schedule window for the third search query would becomputed by subtracting the average duration of the last ten executionsof the third search query from the period of the third search query.Therefore, if the average duration of the last ten executions of thethird search query is twenty-five minutes and the third search query isscheduled to execute once per hour, then the schedule window for thethird search query would be one hour minus twenty-five minutes, orthirty-five minutes.

In some embodiments, search query scheduling program 1930 may be subjectto a maximum scheduled search percentage parameter. This maximumscheduled search percentage parameter may define a maximum percentage ofthe search capacity of data intake and query system 1808 that searchquery scheduling program 1930 may employ for scheduled search queries.Search query scheduling program 1930 may reserve the remaining searchcapacity for ad hoc search queries, where ad hoc search queries are onetime search queries or occasional search queries that are not scheduledto execute periodically. For example, setting the maximum scheduledsearch percentage parameter to 50% would allow up to 50% of the searchcapacity of data intake and query system 1808 and reserve the remaining50% of the search capacity for ad hoc search queries. Similarly, settingthe maximum scheduled search percentage parameter to 70% would allow upto 70% of the search capacity of data intake and query system 1808 andreserve the remaining 30% of the search capacity for ad hoc searchqueries.

The maximum scheduled search percentage parameter is different than thesearch query skewing techniques described herein. In particular, themaximum scheduled search percentage parameter is an empirical value,typically set by a system administrator, that provides enough searchcapacity so that scheduled search queries are timely executed as oftenas possible, while reserving sufficient search capacity for a reasonablenumber of ad hoc search queries. If the maximum scheduled searchpercentage parameter is set too low, then scheduled search queries maynot execute to completion during a particular period, even if dataintake and query system 1808 has sufficient capacity to execute allscheduled search queries to completion. If, on the other hand, themaximum scheduled search percentage parameter is set too high, then peakdemand occurring at the beginning of a period could still overwhelm theresources of data intake and query system 1808 and cause network datapacket loss or other failure modes. By contrast, the search queryskewing techniques described herein automatically skew search queriesover an appropriate period of time without the need for empiricaladjustment of a user-defined parameter, while increasing the likelihoodthat all search queries execute to completion accurately and on time.

FIGS. 20A-20B illustrate example chronographic schedule strings relatedto search queries in accordance with the disclosed embodiments. Eachexample chronographic schedule string (also referred to herein as a“cron schedule string”) illustrated in FIGS. 20A-20B includes fivefields, labeled as “minute,” “hour,” “day,” “month,” and “day of week.”A numeric value in a particular field identifies a particular minute,hour, day, month, or day of week to execute the corresponding searchquery, as indicated by the numeric value. An asterisk in a particularfield indicates that a search query is to execute every minute, hour,day, month, or day of week, according to the particular field thatcontains the asterisk. A value of the form “*/N” in a particular fieldindicates that a search query is to execute at or near the beginning ofeach N minutes, hours, days, months, or days of week, according to theparticular field that contains a value in this form. In general, thenumber ‘N’ in the expression “*/N” is an integer greater than zero.

FIG. 20A illustrates example cron schedule strings 2002, 2004, 2006,2008, and 2010 related to search queries associated with a relativelyhigh skew tolerance. As shown, cron schedule string 2002 includes anasterisk ‘*’ in each field. Cron schedule string 2002 thereby indicatesthat the corresponding search query is to execute each minute of eachhour of each day of each month. In other words, the search querycorresponding to cron schedule string 2002 is to execute at thebeginning of every minute.

Cron schedule string 2004 includes the value “*/N” in the minute fieldand an asterisk ‘*’ in each of the other fields. Cron schedule string2004 thereby indicates that the corresponding search query is to executeevery N minutes of each hour of each day of each month. In other words,the search query corresponding to cron schedule string 2004 is toexecute at the beginning of every N minutes.

Cron schedule string 2006 includes the value ‘0’ in the minute field andan asterisk ‘*’ in each of the other fields. Cron schedule string 2006thereby indicates that the corresponding search query is to execute atminute 0 of each hour of each day of each month. In other words, thesearch query corresponding to cron schedule string 2006 is to execute atthe beginning of every hour.

Cron schedule string 2008 includes the value ‘0’ in the minute field,the value “*/N” in the hour field, and an asterisk ‘*’ in each of theother fields. Cron schedule string 2008 thereby indicates that thecorresponding search query is to execute every N hours of each day ofeach month. In other words, the search query corresponding to cronschedule string 2008 is to execute at the beginning of every N hours.

Finally, cron schedule string 2010 includes the value ‘0’ in each of theminute and hour fields and an asterisk ‘*’ in each of the other fields.Cron schedule string 2010 thereby indicates that the correspondingsearch query is to execute at minute 0 and hour 0 of each day of eachmonth. In other words, the search query corresponding to cron schedulestring 2010 is to execute at the beginning of every day at midnight.

In sum, the cron schedule strings 2002, 2004, 2006, 2008, and 2010correspond to search queries respectively scheduled to execute once perminute, once per N minutes, once per hour, once per N hours, or once perday. In some embodiments, search query scheduling system 1810 may inferthat search queries corresponding to a cron schedule string in any ofthe formats exemplified by cron schedule strings 2002, 2004, 2006, 2008,and 2010 have a relatively high skew tolerance. Search query schedulingsystem 1810 may determine that the periodicity of such search queries ismore important than executing such search queries at the beginning ofeach period. Stated another way, a search query corresponding to cronschedule string 2006 likely corresponds to a search query that shouldexecute once per hour, but not necessarily at the beginning of eachhour. Correspondingly, search query scheduling system 1810 may skewsearch queries corresponding to cron schedule strings in any of the fiveformats illustrated in FIG. 20A over the entire period specified by thecron schedule string. By contrast, search queries corresponding to cronschedule strings that are not in any of the five formats illustrated inFIG. 20A have a relatively low skew tolerance, as shown in FIG. 20B.

FIG. 20B illustrates example cron schedule strings 2052, 2054, 2056,2058, and 2060 related to search queries associated with a relativelylow skew tolerance. As shown, cron schedule string 2052 includes thevalue ‘11’ in the minute field an asterisk in each of the other fields.Cron schedule string 2052 thereby indicates that the correspondingsearch query is to execute at minute 11 of each hour of each day of eachmonth. In other words, the search query corresponding to cron schedulestring 2002 is to execute once per hour at the 11th minute of the hour.

Cron schedule string 2054 includes the value ‘0’ in the minute field,the value “22” in the hour field, and an asterisk ‘*’ in each of theother fields. Cron schedule string 2054 thereby indicates that thecorresponding search query is to execute at hour 22 of each day of eachmonth. In other words, the search query corresponding to cron schedulestring 2054 is to execute once per day at the 10:00 PM, the 22nd hour ofthe day.

Cron schedule string 2056 includes the value ‘0’ in each of the minuteand hour fields, the value “15” in the day field, and an asterisk ‘*’ ineach of the other fields. Cron schedule string 2056 thereby indicatesthat the corresponding search query is to execute at day 15 of eachmonth. In other words, the search query corresponding to cron schedulestring 2056 is to execute once per month on the 15th of the month.

Cron schedule string 2058 includes the value ‘0’ in each of the minute,hour, and day fields, the value “1” in the month field, and an asterisk‘*’ in the day of week field. Cron schedule string 2058 therebyindicates that the corresponding search query is to execute at month 1of each year. In other words, the search query corresponding to cronschedule string 2058 is to execute once per year on January 1st.

Finally, cron schedule string 2060 includes the value ‘0’ in each of theminute, hour, day, and month fields, and the value “6” in the day ofweek field. Cron schedule string 2060 thereby indicates that thecorresponding search query is to execute at day 6 of each week. In otherwords, the search query corresponding to cron schedule string 2060 is toexecute once per week on Saturday (day 6).

In sum, the cron schedule strings 2052, 2054, 2056, 2058, and 2060correspond to search queries respectively scheduled to a particularminute, hour, day, month, or day of week. In some embodiments, searchquery scheduling system 1810 may infer that search queries correspondingto a cron schedule string in any of the formats exemplified by exemplarycron schedule strings 2052, 2054, 2056, 2058, and 2060 have a relativelylow skew tolerance. Search query scheduling system 1810 may determinethat the particular time specified by cron schedule strings 2052, 2054,2056, 2058, and 2060 is as important as the periodicity of such searchqueries. Stated another way, a search query corresponding to cronschedule string 2052 likely corresponds to a search query that shouldexecute at, or relatively close to, 11 minutes after each hour.Correspondingly, search query scheduling system 1810 may skew searchqueries corresponding to cron schedule strings in any of the fiveformats as illustrated and exemplified in FIG. 20B over a relativelyshort time period, such as sixty seconds.

In some embodiments, cron schedule strings exemplified in FIGS. 20A-20Bmay be employed in conjunction with an allow skew setting to determine amaximum allowable skew for each corresponding search query, as nowdescribed.

FIG. 21 illustrates example allow skew settings 2102, 2104, 2106, 2108,2110, and 2112 in accordance with the disclosed embodiments. In general,allow skew settings include a numerical value and a unit specifier.These unit specifiers include, without limitation, ‘%’ (percent), ‘s’(seconds), ‘m’ (minutes), ‘h’ (hours), and ‘d’ (days). Alternative unitspecifiers for seconds include, without limitation, “sec,” “second,”“secs,” and “seconds.” Alternative unit specifiers for minutes include,without limitation, “min,” “minute,” “mins,” and “minutes.” Alternativeunit specifiers for hours include, without limitation, “hr,” “hour,”“hrs,” and “hours.” Alternative unit specifiers for days include,without limitation, “day,” and “days.” The allow skew setting isemployed to determine the maximum allowable skew for one or morecorresponding search queries.

As shown in FIG. 21 , allow skew settings 2102, 2104, and 2106 eachinclude a numeric value followed by a ‘%’ unit specifier. In such case,the maximum allowable skew is computed by multiplying the allow skewsetting by the period indicated by the corresponding cron schedulestring.

Allow schedule setting 2102 includes the value ‘50’ followed by a ‘%’unit specifier. Allow schedule setting 2102 thereby indicates that themaximum allowable skew is computed by multiplying 50% by the valueindicated in the corresponding cron schedule string. The cron schedulestring corresponding to allow schedule setting 2102 indicates a scheduleof once per minute. Therefore, the maximum allowable skew for allowschedule setting 2102 is 50% of one minute, or 30 seconds.

Allow schedule setting 2104 includes the value ‘50’ followed by a ‘%’unit specifier. Allow schedule setting 2104 thereby indicates that themaximum allowable skew is computed by multiplying 50% by the valueindicated in the corresponding cron schedule string. The cron schedulestring corresponding to allow schedule setting 2104 indicates a scheduleof once per ten minutes. Therefore, the maximum allowable skew for allowschedule setting 2104 is 50% of ten minutes, or five minutes.

Allow schedule setting 2106 includes the value ‘75’ followed by a ‘%’unit specifier. Allow schedule setting 2106 thereby indicates that themaximum allowable skew is computed by multiplying 75% by the valueindicated in the corresponding cron schedule string. The cron schedulestring corresponding to allow schedule setting 2106 indicates a scheduleof once per hour. Therefore, the maximum allowable skew for allowschedule setting 2106 is 75% of one hour, or forty-five minutes.

Allow schedule setting 2108 includes the value ‘5’ followed by an ‘m’unit specifier. Allow schedule setting 2108 thereby indicates that themaximum allowable skew is five minutes. The corresponding cron schedulestring may be of any form illustrated in FIG. 20A.

Allow schedule setting 2110 includes the value ‘1’ followed by an ‘h’unit specifier. Allow schedule setting 2110 thereby indicates that themaximum allowable skew is one hour. The corresponding cron schedulestring may be of any form illustrated in FIG. 20A.

Finally, allow schedule setting 2112 includes the value ‘60’ without anyfollowing unit specifier. Therefore, allow schedule setting 2112 isconsidered to be an error, and skewing is disabled. Without a unitspecifier, the cannot determine whether allow schedule setting 2112 isintended to indicate, for example, 60%, 60 seconds, 60 minutes, or someother value.

FIGS. 22A-22B illustrate example visualizations of system load beforeand after search query skewing is enabled in accordance with thedisclosed embodiments.

In particular, FIG. 22A illustrates an example visualization of systemload before search query skewing is enabled. System load represents anyone or more technically feasible metrics related to usage of data intakeand query system 1808 resources. These resources include, withoutlimitation, compute processing capacity, input/output capacity, andtotal network bandwidth. As shown in FIG. 22A, a significant increase inload occurs at time 2204, corresponding to 10:11 AM at the beginning ofthe minute. Similarly, a significant increase in load occurs at time2202, corresponding to 10:10 AM at the beginning of the minute. Duringthe period between time 2202 and 2204, the system experiences relativelylow load. In addition, a significant increase in load occurs at time2206, corresponding to 10:12 AM at the beginning of the minute. Duringthe period between time 2204 and 2206, the system experiences relativelylow load.

FIG. 22B illustrates an example visualization of system load aftersearch query skewing is enabled. As shown, times 2252, 2254, and 2256,respectively correspond to 11:00 AM, 11:01 AM, and 11:02 AM, at thebeginning of each of these minutes. A moderate increase in load isindicated at time 2252 and time 2254. However, FIG. 22B indicates thatthe load is balanced over the entire period between time 2252 and time2254, as compared with the period between time 2202 and 2204 of FIG.22A. Similarly, FIG. 22B indicates that the load is balanced over theentire period between time 2254 and time 2256, as compared with theperiod between time 2204 and 2206 of FIG. 22A. Correspondingly, thelikelihood that data intake and query system 1808 experiences excessiveload, resulting in network data packet loss or other failure modes, isreduced when skewing of search queries is enabled (FIG. 22B) relative towhen skewing is not enabled (FIG. 22A).

FIG. 23 is a flow diagram of method steps for skewing search queries inaccordance with the disclosed embodiments. Although the method steps aredescribed in conjunction with the systems of FIGS. 1-2, 10-11, and 18-19, persons of ordinary skill in the art will understand that any systemconfigured to perform the method steps, in any order, is within thescope of the present invention.

As shown, a method 2300 begins at step 2302, where a search queryscheduling program 1930 executing on a search query scheduling system1810 classifies a search query based on a corresponding cron schedulingstring. If the cron scheduling string is in one of five particularformats that indicates the search query is scheduled for execution onceevery minute, once every N minutes, one every hour, once every N hours,or once per day, then the search query scheduling program 1930classifies the search query as having a relatively high skew tolerance.If the cron scheduling string is in format that is different from one ofthese five particular formats, then the search query scheduling program1930 classifies the search query as having a relatively low skewtolerance.

At step 2304, the search query scheduling program 1930 computes amaximum allowable skew amount for the search query scheduled to executeon a data intake and query system 1808. If the search query isclassified as having a relatively high skew tolerance, then the searchquery scheduling program 1930 computes the maximum allowable skew amountbased on a cron scheduling string associated with the search query. Ingeneral, the search query scheduling program 1930 sets the maximumallowable skew amount as the time between one scheduled execution of thesearch query and a consecutive scheduled execution of the same searchquery. If the search query is classified as having a relatively low skewtolerance, then the search query scheduling program 1930 sets themaximum allowable skew amount to a relatively low fixed value, such assixty seconds.

At step 2306, the search query scheduling program 1930 modifies themaximum allowable skew amount based on an allow skew setting. If theallow skew value specifies a percentage value, then the search queryscheduling program 1930 multiplies the maximum allowable skew by thepercentage value to generate the modified maximum allowable skew amount.If the allow skew value specifies a specific duration of time, then thesearch query scheduling sets the modified maximum allowable skew amountas the specific duration of time.

At step 2308, the search query scheduling program 1930 computes a hashvalue associated with the search query. The hash value is based on oneor more attributes of the search query that are relatively likely to beunique between one search query and another search query, and arerelatively unlikely to change over time. For example, the search queryscheduling program 1930 could compute a hash value for the search querybased on any one or more of the name or title of the search, the textualdescription of the search, and the search string that specifies theparameters of the search.

At step 2310, the search query scheduling program 1930 computes a finalskew amount for the search query. The search query scheduling program1930 may compute the final skew value as: final skew value=hash valuemod maximum allowable skew, where “mod” represents the modulo operation.

At step 2312, the search query scheduling program 1930 adjusts thepriority of the search query based on a schedule window. If the schedulewindow for the search query is set to zero, then the search query wouldbe considered to be of relatively greater priority and would bescheduled according to the search query skewing techniques describedherein. If the schedule window for the search query is set to aparticular duration, then the search query would be considered to be oflesser priority. The search query would be a candidate to deferexecution for a duration not to exceed the duration of the schedulewindow. If the schedule window for the search query is set to auto orautomatic, then the third search query would be considered to be oflesser priority. The schedule window for the search query would becomputed by subtracting the average duration of the last ten executionsof the third search query from the period of the third search query.Therefore, if the average duration of the last ten executions of thethird search query is twenty-five minutes and the third search query isscheduled to execute once per hour, then the schedule window for thethird search query would be one hour minus twenty-five minutes, orthirty-five minutes. The method 2300 then terminates.

In sum, the dispatch and execution times for scheduled search queries ina data intake and query system are skewed so that the scheduled searchqueries do not all execute at the same time. Each search query isdelayed by a computed amount. A maximum allowable skew amount isdetermined based on the frequency of execution of the search query, asdefined by the chronographic scheduling string corresponding to thesearch query. The maximum allowable skew amount may be modified bymultiplying the maximum allowable skew by a percentage value specifiedby an allow skew setting. Alternatively, the allow skew setting mayspecify a specific duration that defines the maximum allowable skewamount. A hash value is computed for each search query based on certainparameters related to the search, such as the name or title of thesearch, the textual description of the search, and the search stringthat specifies the parameters of the search. A final skew value for eachsearch query is computed based on the corresponding hash value andmaximum allowable skew amount.

At least one advantage of the disclosed techniques is that searchqueries scheduled to occur simultaneously are skewed over a period oftime. As a result, the demand for computing and network resources is notconcentrated at certain points in time, but rather is distributed over aperiod of time. By distributing the demand for computing and networkresources over a period of time, the likelihood of network data packetloss or other failure modes due to the excess demand is reduced relativeto prior approaches.

The descriptions of the various embodiments have been presented forpurposes of illustration, but are not intended to be exhaustive orlimited to the embodiments disclosed. Many modifications and variationswill be apparent to those of ordinary skill in the art without departingfrom the scope and spirit of the described embodiments.

Aspects of the present embodiments may be embodied as a system, methodor computer program product. Accordingly, aspects of the presentdisclosure may take the form of an entirely hardware embodiment, anentirely software embodiment (including firmware, resident software,micro-code, etc.) or an embodiment combining software and hardwareaspects that may all generally be referred to herein as a “module” or“system.” Furthermore, aspects of the present disclosure may take theform of a computer program product embodied in one or more computerreadable medium(s) having computer readable program code embodiedthereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

Aspects of the present disclosure are described above with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of thedisclosure. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, enable the implementation of the functions/acts specified inthe flowchart and/or block diagram block or blocks. Such processors maybe, without limitation, general purpose processors, special-purposeprocessors, application-specific processors, or field-programmable gatearrays.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

While the preceding is directed to embodiments of the presentdisclosure, other and further embodiments of the disclosure may bedevised without departing from the basic scope thereof, and the scopethereof is determined by the claims that follow.

What is claimed is:
 1. A computer-implemented method, comprising:associating a first set of queries with a skew amount, wherein the firstset of queries is scheduled to be performed during each occurrence of afirst period, and the skew amount is associated with a first portion ofthe first period; computing an average duration of a set of executionsof the first query during occurrences of the first period; determining afirst scheduling window associated with a first query in the first setof queries based on the average duration, wherein the first schedulingwindow indicates how long a corresponding search query can be delayed;and rescheduling the first query for execution during the first portionof the first period based on the skew amount and the first schedulingwindow, wherein the first query is executed via one or more processors,based on the rescheduling of the first query for execution, during thefirst portion of the first period.
 2. The computer-implemented method ofclaim 1, further comprising: rescheduling a second query in the firstset of queries based on the skew amount, wherein the second query isscheduled in the first portion of the first period before the firstquery.
 3. The computer-implemented method of claim 1, furthercomprising: determining that the first query is not executed during thefirst portion of the first period; and rescheduling the first query forexecution during a portion of a second period based on the skew amountand the first scheduling window.
 4. The computer-implemented method ofclaim 1, further comprising: determining that the first query is notexecuted during the first portion of the first period; determining thatthe first scheduling window has expired; and rescheduling the firstquery for execution during a portion of a second period based on theskew amount.
 5. The computer-implemented method of claim 1, whereindetermining the first scheduling window comprises: subtracting theaverage duration from the first period to generate the first schedulingwindow.
 6. The computer-implemented method of claim 1, furthercomprising adjusting a priority associated with a first query based onthe first scheduling window.
 7. The computer-implemented method of claim1, further comprising: determining the first portion of the first periodbased on a maximum skew amount associated with the first set of queries.8. The computer-implemented method of claim 7, further comprising:multiplying the maximum skew amount by a percentage value specified by askew setting associated with the first set of queries, wherein thepercentage value indicates a percent of the first period during whichthe first set of queries is to be executed.
 9. The computer-implementedmethod of claim 7, further comprising: modifying the maximum skew amountbased on an allow skew setting.
 10. The computer-implemented method ofclaim 1, further comprising: computing a hash value for the first querybased on parameters associated with the first query, wherein theparameters associated with the first query include at least one of: atitle associated with the first query, a description associated with thefirst query, or a search string associated with the first query.
 11. Oneor more non-transitory computer-readable media storing instructionsthat, when executed by one or more processors, cause the one or moreprocessors to perform the steps of: associating a first set of querieswith a skew amount, wherein the first set of queries is scheduled to beperformed during each occurrence of a first period, and the skew amountis associated with a first portion of the first period; computing anaverage duration of a set of executions of the first query duringoccurrences of the first period; determining a first scheduling windowassociated with a first query in the first set of queries based on theaverage duration, wherein the first scheduling window indicates how longa corresponding search query can be delayed; and rescheduling the firstquery for execution during the first portion of the first period basedon the skew amount and the first scheduling window, wherein the firstquery is executed via one or more processors based on the reschedulingof the first query for execution during the first portion of the firstperiod.
 12. The one or more non-transitory computer-readable media ofclaim 11, further storing instructions that, when executed by the one ormore processors, cause the one or more processors to perform the stepsof: rescheduling a second query in the first set of queries based on theskew amount, wherein the second query is scheduled in the first portionof the first period before the first query; and adjusting a priorityassociated with the first query based on the first scheduling window.13. The one or more non-transitory computer-readable media of claim 11,further storing instructions that, when executed by the one or moreprocessors, cause the one or more processors to perform the steps of:determining that the first query is not executed during the firstportion of the first period; and rescheduling the first query forexecution during a portion of a second period based on the skew amountand the first scheduling window.
 14. The one or more non-transitorycomputer-readable media of claim 11, wherein determining the firstscheduling window comprises: subtracting the average duration from thefirst period to generate the first scheduling window.
 15. The one ormore non-transitory computer-readable media of claim 11, further storinginstructions that, when executed by the one or more processors, causethe one or more processors to perform the steps of: computing a hashvalue for the first query based on one or more parameters associatedwith the first query, and computing a skew value associated with thefirst query, wherein the skew value is the hash value associated withthe first query modulo a maximum skew amount associated with the firstquery.
 16. The one or more non-transitory computer-readable media ofclaim 11, wherein: the first query is directed to a set of events, andeach event included in the set of events includes (i) raw machine data,and (ii) an associated timestamp derived from the raw machine data. 17.The one or more non-transitory computer-readable media of claim 16,wherein a late-binding schema is applied to event data associated withthe set of events.
 18. A computing device, comprising: one or morememories that include a query scheduling program; and one or moreprocessors that are coupled to the one or more memories and, whenexecuting the query scheduling program, are configured to: associate afirst set of queries with a skew amount, wherein: the first set ofqueries is scheduled to be performed during each occurrence of a firstperiod, and the skew amount is associated with a first portion of thefirst period; compute an average duration of a set of executions of thefirst query during occurrences of the first period; determine a firstscheduling window associated with a first query in the first set ofqueries based on the average duration, wherein the first schedulingwindow indicates how long a corresponding search query can be delayed;and reschedule the first query for execution during the first portion ofthe first period based on the skew amount and the first schedulingwindow, wherein the first query is executed via one or more processorsbased on the rescheduling of the first query for execution during thefirst portion of the first period.
 19. The computing device of claim 18,wherein the one or more processors are further configured to: reschedulea second query in the first set of queries based on the skew amount,wherein the second query is scheduled in the first portion of the firstperiod before the first query; and adjust a priority associated with thefirst query based on the first scheduling window.
 20. The computingdevice of claim 18, wherein determining the first scheduling windowcomprises: subtracting the average duration from the first period togenerate the first scheduling window.